Ackermann, F. (1999). Airborne laser scanning - present status and
future expectations. Isprs Journal of Photogrammetry and Remote
Sensing, V54, (N2-3): 64-67.
Keywords: airborne laser scanning
Synopsis: This article describes present applications, makes comparisons
to photogrammetry, and discusses future prospects for Airborne Laser Scanning
(ALS).
Adams, J., (1999). Space Imagery and GIS in Oklahoma State Transportation
Planning (power point file). A National Forum on Remote Sensing Applications
to Transportation, May 11-12, Washington DC
http://scitech.dot.gov/reeng/sensmsrm/rmtsense/sbrsagnd.html,
Ahmed, F.; M. A. Karim; M. S. Alam (1995). Wavelet transform-based
correlator for the recognition of rotationally distorted images. Optical
Engineering, 34, (11): 3187-92.
Keywords: Adaptive filters; Feature extraction; Fourier transform
optics; Image recognition; Optical correlation; Optical signal detection;
Remote sensing; Wavelet transforms; Wavelet-based joint transform correlator;
Rotation-invariant pattern recognition; Optical image processing; Rotationally
distorted images recognition; Optimal filter parameters set; Mother wavelet
filter; Composite reference feature; Noiseless environments; Noisy environments;
Filter modulations; Discrimination improvement; Optical target detection
; Thresholding
Original Abstract: A novel wavelet-based joint transform correlator
(WJTC) for rotation-invariant pattern recognition and applications in optical
image processing and remote sensing is investigated. First an optimal set
of filter parameters and a mother wavelet filter are selected. These are
used to extract features at different resolution from a set of rotationally
distorted training images. Then a composite reference feature is formulated
from these features for use in the WJTC. Simulation results for both noisy
and noiseless environments are presented to verify the effectiveness of
this technique.
Aizenberg, I. N., (1996). Extraction of the small details on the
noisy images and their sharpening: implementation on the CNN. 1996
Fourth IEEE International Workshop on Cellular Neural Networks and their
Applications Proceedings (CNNA-96) (Cat. No.96TH8180) 1996 (CNNA-96) Seville,
Spain 24-26 June 1996
New York, NY, USA IEEE, pp.31-6.
Keywords: Cellular neural nets; Computer vision; Edge detection;
Feature extraction; Filtering theory; Image enhancement; Remote sensing;
Noisy images; Impulse noise filtering; Frequency amplification; Satellite
images; Cellular neural network ; Gray scale images
Original abstract: Applications of the algorithms of impulse noise
filtering, edge detection, high and medium frequencies amplification to
improve the quality of gray-scale images, especially of satellite images,
are considered. All the algorithms presented are implemented on the cellular
neural network (the classical CNN type or based on universal binary neurons).
The strategy of processing small-detailed images using the noise filtering
without smoothing, edge detection and extraction of the small or other
important details on the complex image background, has been carried out.
Al-Nuaimy, W.; Y. Huang; A. Eriksen; V. T. Nguyen (2000). Automatic
feature selection for unsupervised image segmentation. Applied Physics
Letters, 77, (8): 1230-2.
Keywords: Feature extraction; Image classification; Image segmentation;
Image sequences; Image texture; Radar imaging; Radar theory; Automatic
feature selection; Unsupervised image segmentation; Computational bottleneck;
Image data; Remote sensing; Medical imaging; Automatic analysis; Automatic
interpretation; Classification tasks; Segmentation tasks; Multivariate
data; Dimensionality; General-purpose unsupervised image segmentation system;
Automatic detection; Image regions; Visual texture properties; Suboptimal
feature selection procedure; Automatic selection; Texture features; Segmentation;
Ground-penetrating radar images; Automatic subsurface reports; Feature
set ; Computation time
Original Abstract: A computational bottleneck is often imposed by the
volume of image data generated in disciplines such as remote sensing and
medical imaging, especially in situations where automatic analysis or interpretation
is required. Segmentation and classification tasks that utilize multivariate
data can be impeded by this dimensionality. A general-purpose unsupervised
image segmentation system is presented here for the automatic detection
of image regions exhibiting different visual texture properties. A suboptimal
feature selection procedure is proposed to automatically select the set
of texture features best suited for the particular application. Results
are presented for the segmentation of ground-penetrating radar images for
generating automatic subsurface reports. The reduction in the size of the
feature set both reduces the computation time and improves the accuracy.
Aloisi, R.; Y. Grabit, (1996). Multispectral image resolution enhancement
to improve efficiency of spectral analysis algorithms. Algorithms for
Multispectral and Hyperspectral Imagery II Orlando, FL, USA 9-11 April
1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.160-7.
Keywords: Correlation methods; Feature extraction; Geophysics computing;
Image enhancement; Image resolution; Remote sensing; Sensor fusion; Spectral
analysis; Wavelet transforms; Multispectral image; ARSIS method; Wavelet
transform; Spatial resolution; Structure extraction; Spatial correlation;
SPOT data; Panchromatic aerial imagery ; 10 m
Original abstract: The ARSIS method (ARSIS: French acronym for "spatial
resolution enhancement by injection of structures") is based on multiresolution
analysis techniques, especially on the wavelet transform. Its goal is to
increase the spatial resolution of an image using the geometric structures
extracted from a higher resolution image, given a sufficient level of spatial
correlation between these two images. The method was developed on SPOT
data for the processing of 10 meter resolution multispectral images. This
paper presents the studies conducted for the adaptation of ARSIS to allow
the fusion of SPOT XS data with high-resolution panchromatic aerial imagery.
The models used in the original method were revealed to be ineffective
for important resolution differences between the images and various methods
were tested to obtain acceptable results.
Alquier, L.; P. Montesinos, (1996). Perceptual grouping and active
contour functions for the extraction of roads in satellite pictures.
Image and Signal Processing for Remote Sensing III Taormina, Italy 23-25
Sept. 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.153-63.
Keywords: Dynamic programming; Edge detection; Feature extraction;
Image segmentation; Interactive systems; Noise; Remote sensing; Perceptual
grouping; Active contour functions; Road extraction; Satellite pictures;
Crest lines detection; Visual properties; Quality function; Curvature;
Grey levels; Co-circularity; Noisy environments; High level interpretation
process ; Interactive decision system
Original abstract: We present a, new method for perceptual grouping
of pixels into roads after crest lines detection in satellite pictures.
First the visual properties expected from the groupings are modelled as
a quality function similar to active contour functions. They involve curvature,
grey levels and co-circularity. This function is computed recursively and
optimized from a local to global level with an algorithm related to dynamic
programming. The final groupings are then selected according to their global
quality. Applied to satellite images, the method proved its adaptability
and its robustness to noisy environments. The results showed how the use
of visual properties can provide an effective segmentation with no prior
knowledge of the scene. This segmentation can be used to initialize a high
level interpretation process or give a first description of the scene to
an interactive decision system.
Ambrico, P. F.; A. Amodeo; P. Di Girolamo; N. Spinelli (2000). Sensitivity
analysis of differential absorption lidar measurements in the mid-infrared
region. Applied Optics, 39, (36): 6847-65.
Keywords: Air pollution measurement; Atmospheric optics; Atmospheric
techniques; Optical radar; Remote sensing by laser beam; Sensitivity analysis;
Differential absorption lidar measurements; Mid-infrared region; Laser
sources; Tunable laser sources; IR spectral region; Differential absorption
lidar; Atmospheric pollutants; Absorption lines; Emissions; Industrial
plants; Urban areas; DIAL measurements; Interference; Absorption cross
section; Optical depth; HCl; co; CO/sub 2/; NO/sub 2/; H/sub 2/O; O/sub
2/; Methane; Sensitivity study; Clean air; Urban polluted air; Emission
; Incinerator stack
Original Abstract: The availability of new laser sources that are tunable
in the IR spectral region opens new perspectives for differential absorption
lidar (DIAL) measurements. A region of particular interest is located in
the near IR, where some of the atmospheric pollutants have absorption lines
that permit monitoring of emissions from industrial plants and in urban
areas. In DIAL measurements, the absorption lines for the species to be
measured must be carefully chosen to prevent interference from other molecules,
to minimize the dependence of the absorption cross section on temperature,
and to optimize the measurements with respect to the optical depth. We
analyze the influence of these factors and discuss a set of criteria for
selecting the best pairs of wavelengths ( lambda /sub ON/ and lambda /sub
OFF/) to be used in DIAL measurements of several molecular species (HCl,
CO, CO/sub 2/, NO/sub 2/, CH/sub 4/, H/sub 2/O, and O/sub 2/). Moreover,
a sensitivity study has been carried out for selected lines in three different
regimes: clean air, urban polluted air, and emission from an incinerator
stack.
Ancis, M.; M. Murroni; D. D. Giusto; M. Petrou, (1999). Region-based
remote-sensing image compression in the wavelet domain. IEEE 1999 International
Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293)
IGARSS'99 Hamburg, Germany 28 June-2 July 1999
Piscataway, NJ, USA IEEE, v.4, pp.2054-6.
Keywords: Data compression; Geophysical signal processing; Geophysical
techniques; Image coding; Image texture; Remote sensing; Terrain mapping;
Vegetation mapping; Wavelet transforms; Geophysical mesurement technique;
Image processing; Land surface; Agriculture; Image compression; Wavelet
domain; Region-based method; Image region; Generic algorithm; Wavelet transform;
Image preprocessing; Forest ; Urban
Original abstract: This paper argues that texture regions in remotely
sensed images of the Earth are often of no interest to projects for example,
concerned with agricultural applications. These regions require a large
number of bits to be encoded. It is proposed that they can be identified
using a generic algorithm that identifies the boundaries of textured regions
irrespective of their class, and removed from the encoding process. The
rest of the regions which may be of interest to the specific application,
may be encoded using 1D wavelet transform applied to the string of pixels
created by raster scanning the region. This approach can help remove the
bottleneck of image down-loading from micro-satellites in low Earth orbits,
because these satellites can obtain hundreds of images in an orbit but
they can only download a few of them during each pass over the tracking
station. The proposed approach can be fully implemented for on-board image
preprocessing before the down-loading, for cases that urban and forest
regions (textured regions) in the images are of no interest.
Anderson, S. G. (1995). Remote Sensing - Diode-Pumped Parametric
Oscillator/Laser Simplifies Lidar System. Laser Focus World,
V31, (N4): 46-47.
Keywords: Optics/Acoustics
Apostolopoulou, A.; N. Sekopoulos; C. Papandreou; P. Klimis, (1997).
Inventory and operation of the road and public transport networks in
Attica region. Transportation Systems 1997. (TS'97). Proceedings of
the 8th IFAC/IFIP/IFORS. Transportation Systems 1997 (3 vol.) Chania, Greece
16-18 June 1997
Oxford, UK Pergamon, pp.443-7 vol.1.
Keywords: Database management systems; Geographic information systems;
Strategic planning; Traffic information systems; Transportation; Travel
industry; Road networks; Public transport networks; Attica region; Public
transportation systems; Urban subsystems; Suburban subsystems; Inter-urban
subsystems; Buses; Rail; ISAP Metro; Metro Development Study; Data collection;
Inventory; Long-term planning; Data processing; gis ; Geographic information
system
Original abstract: The existing road network and the public transportation
systems in Attica comprising urban, suburban and inter-urban subsystems
(buses, rail, ISAP Metro, terminals, etc.) form the environment into which
the two new Metro lines, presently under construction and their future
extensions or new lines will have to merge and integrate in the best possible
way. In the context of the Metro Development Study (MDS), extensive data
collection and inventory of the infrastructure and operation characteristics
of the transportation systems is essential for the evaluation of existing
conditions and for the development of a database required in long-term
planning. The paper presents in brief the data collection and processing
procedures, the development of related databases and GIS applications,
and finally some of the significant findings, results and conclusions regarding
the operation of the transportation systems.
Arai, K.; Y. Terayama; T. Arata, (1995). Image classification based
on beta distribution for SAR image. 1995 International Geoscience and
Remote Sensing Symposium, IGARSS '95. Quantitative Remote Sensing for Science
and Applications (Cat. No.95CH35770) Firenze, Italy 10-14 July 1995
New York, NY, USA IEEE, pp.1263-5 vol.2.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Image texture; Radar imaging; Remote
sensing by radar; Synthetic aperture radar; Geophysical measurement technique;
Land surface; Terrain mapping; Radar remote sensing; SAR imagery; Image
processing; Beta distribution; SAR image; Maximum likelihood decision rule;
Probability density function; Multivariate normal distribution; Local least
square estimator; Sigma filter; Weighting filter ; Speckle noise reduction
Original abstract: A new method for SAR image classification is proposed.
The method is based on maximum likelihood decision rule with texture features
and takes into account the probability density function of texture features.
The experimental results show the proposed method is superior to the existing
maximum likelihood method with multivariate normal distribution. 2.28 to
5.16% of improvements are observed with real SAR image. Effects of local
least square estimator, sigma and weighting filters for speckle noise reduction
on classification performance are clarified. The results show that 7.1
to 12.04 % of improvements on the classification performance are observed.
Arentze, T. A.; H. J. P. Timmermans (2000). A spatial decision support
system for retail plan generation and impact assessment. Transportation
Research Part C (Emerging Technologies), 8C, (1-6): 361-80.
Keywords: Decision support systems; Geographic information systems;
Retail data processing; Town and country planning; Transportation; Visual
databases; Spatial decision support system; Retail plan generation; Retail
plan impact assessment; Land-use planning; Transportation planning; Location
Planner ; Optimization
Original Abstract: Current geographic information systems typically
offer limited analytical capabilities and lack the flexibility to support
spatial decision making effectively. Spatial decision support systems aim
to fill this gap. Following this approach, this paper describes an operational
system for integrated land-use and transportation planning called Location
Planner. The system integrates a wide variety of spatial models in a flexible
and easy-to-use problem solving environment. Users are able to construct
a model out of available components and use the model for impact analysis
and optimization. Thus, in contrast to existing spatial decision support
systems, the proposed system allows users to address a wide range of problems.
The paper describes the architecture of the system and an illustrative
application. Furthermore, the potentials of the system for land-use and
transportation planning are discussed.
Asano, K.; A. Hoyano, (1998). Application of a new spherical thermography
technique to monitoring of outdoor long wave radiant fields. Infrared
Technology and Applications XXIV San Diego, CA, USA 19-24 July 1998
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.317-24.
Keywords: Environmental engineering; Infrared imaging; Remote sensing;
Spherical thermography technique; Outdoor long wave radiant fields monitoring;
Urban street spaces; Above-ground condition differences; Design characteristics;
Thermal comfort; Mean radiant temperature; Vector radiant temperature ;
Environment measuring system
Original abstract: This paper describes the evaluation of the long
wave radiant field using the spherical thermography technique we developed
in previous studies. Four urban street spaces were chosen for comparison
based mainly on differences in the above-ground conditions. Evaluations
were conducted during good weather on summer days. The measurement results
indicate that the long wave radiant field is directly influenced by the
design characteristics of the urban space. The present study confirmed
the usefulness of spherical thermography for evaluating the long wave radiant
field.
Asrar, G., (1999). NASA Earth Science Enterprise: Remote Sensing
Research and Commercialization (Power Point File). A National Forum
on Remote Sensing Applications to Transportation, May 11-12, 1999, Washington
DC
http://scitech.dot.gov/reeng/sensmsrm/rmtsense/sbrsagnd.html,
Axelsson, P. (1999). Processing of laser scanner data - algorithms
and applications. ISPRS Journal of Photogrammetry and Remote Sensing,
54, (2-3): 138-147.
Keywords: processing airborne laser scanning
Synopsis: Describes laser scanner data, processing methods, classification
of buildings and electric power lines.
Original Abstract: Airborne laser scanning systems are opening new
possibilities for surveys and documentation of difficult areas and objects,
such as dense city areas, forest areas and electrical power lines. Laser
scanner systems available on the market are presently in a fairly mature
state of art while the processing of airborne laser scanner data still
is in an early phase of development. To come from irregular 3D point clouds
to useful representations and formats for an end-user requires continued
research and development of methods and algorithms for interpretation and
modelling. This paper presents some methods and algorithms concerning filtering
for determining the ground surface, DEM, classification of buildings for
3D City Models and the detection of electrical power lines. The classification
algorithms are based on the Minimum Description Length criterion. The use
of reflectance data and multiple echoes from the laser scanner is examined
and found to be useful in many applications.
Babey, S. K.; C. D. Anger, (1996). Potential for the application
of airborne hyperspectral remote sensing techniques to industrial inspection.
Three-Dimensional and Unconventional Imaging for Industrial Inspection
and Metrology Philadelphia, PA, USA 23-25 Oct. 1995
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.302-7.
Keywords: Automatic optical inspection; Calibration; CCD image sensors;
Feature extraction; Image resolution; Image sampling; Infrared imaging;
Infrared spectrometers; Process control; Quality control; Remote sensing;
Spectroscopy computing; Visible spectrometers; Airborne hyperspectral remote
sensing; Industrial inspection application; Optical remote sensing; High
spectral resolution imagery; Optimal selection; High speed acquisition;
Radiometric calibration; Conversion to spectral reflectances; Geometric
correction; Discrimination of subtle features; Scene image sampling; Datacube;
2D image sensor; Spatial mode; Spectral mode; Preprocessing; Digital elevation
model; Postprocessing; Algorithms; qc; Visible ; Near infrared
Original abstract: Airborne optical remote sensing has recently seen
an evolution from limited spectral discrimination into high spectral resolution
imagery. Hyperspectral imagers now have the ability to sample a scene at
both high spatial and high spectral resolution, permitting optimal selection
from the available information to meet specific applications. Techniques
have been developed which provide for high speed acquisition, radiometric
calibration, conversion to spectral reflectances, geometric correction
and interpretation of airborne hyperspectral data. The same technology
currently in use for discrimination of subtle features in airborne scenes
is available for industrial inspection applications which can benefit from
combined spectral reflectance and imaging information.
Babic, N. C., (2000). GIS supported management information systems
in road administration domain. Second International Conference on Management
Information Systems Incorporating GIS and Remote Sensing. Management Information
Systems 2000 Proceedings of Management Information Systems Udine, Italy
May 2000
Southampton, UK WIT Press, pp.177-84.
Keywords: Civil engineering computing; Construction industry; Data
visualisation; Geographic information systems; Management information systems;
Transportation; gis; Road administration; Business; Data abstraction; Decision
makers; Road construction; Map generation; Road maintenance ; Geographic
information system
Original abstract: The main goal that should guide development of management
information systems is development of tools that are useful for busy practitioners
to access and use information. Practitioners need methods to answer questions
and make decisions. At the same time, a trend of increasing specialisation
is evident in today's business. Therefore, applications should be tailored
to help the user accomplish related tasks quickly. It is important to present
the user with just what he/she needs and when it is needed. Also, an appropriate
level of data abstraction should be provided to decision makers. Visualisation
plays a very important role in this process. In the construction industry,
especially road construction, visualisation is implemented with the help
of GIS tools. However, we must not restrict GIS usage to map generation.
Data from different sources, like spreadsheets and databases, can be correlated
through maps. This ability to get the big picture allows planners to make
better decisions in a more holistic manner. To accomplish this goal, GIS
has to be implemented to support existing decision-making process, and
not as a separate stand-alone utility. We illustrate all these principles
through an example from road maintenance.
Baillard, C.; O. Dissard; O. Jamet; H. Maitre, (1996). Detection
of above-ground in urban areas: application to DTM generation. Image
and Signal Processing for Remote Sensing III Taormina, Italy 23-25 Sept.
1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.129-40.
Keywords: Feature extraction; Geographic information systems; Image
matching; Image segmentation; Markov processes; Remote sensing; Stereo
image processing; Above-ground detection; Urban areas; DTM generation;
Stereoscopic images; Stereoscopic matching stage; Digital surface model;
DSM segmentation; Sloping ground; Digital terrain models; Above-ground
extraction; Aerial imagery; Markov random field ; Cartographic databases
Original abstract: A new approach to the detection of above-ground
from a pair of stereoscopic images in a general urban context is proposed.
It includes a stereoscopic matching stage well-adapted to our task in order
to provide a digital surface model (DSM). Then a segmentation of the DSM
is performed, and regions are classified as ground or above-ground. The
interest of the method is its ability to manage extended above-ground with
several heights and any shape, as well as the case of a sloping ground.
An application to digital terrain models (DTM) generation in urban areas
is discussed. Assessment of both above-ground extraction and DTM generation
on difficult scenes shows the feasibility of the approach.
Baillard, C.; O. Dissard; H. Maitre, (1998). Segmentation of urban
scenes from aerial stereo imagery. Proceedings. Fourteenth International
Conference on Pattern Recognition (Cat. No.98EX170) Brisbane, Qld., Australia
16-20 Aug. 1998
Los Alamitos, CA, USA IEEE Comput. Soc
Proceedings., pp.1405-7 vol.2.
Keywords: Image reconstruction; Image segmentation; Remote sensing;
Stereo image processing; Urban scenes; Aerial stereo imagery; Focusing
strategy; 3D reconstruction; Above-ground objects; Radiometric analyses;
Adjacent objects ; Slopes
Original abstract: Presents a focusing strategy for the 3-D reconstruction
of urban scenes from aerial stereo pairs. It consists in segmenting the
scene into above-ground objects (buildings or vegetation), and it relies
on 3-D and radiometric analyses. The classification is able to cope with
extended above-ground, adjacent objects, slopes, and it is robust to image
and scene variability.
Baltsavias, E. P. (1999). Airborne laser scanning: existing systems
and firms and other resources. Isprs Journal of Photogrammetry and
Remote Sensing, V54, (N2-3): 164-198.
Keywords: systems airborne laser scanning, resources
Original Abstract: This article gives an overview of resources on airborne
laser scanning (ALS). The main emphasis is on existing systems and firms,
especially commercial ones. Through a very time-consuming search and with
the help of numerous persons from firms, organisations and other colleagues,
a quite complete survey of existing commercial systems, including detailed
system parameters, has been compiled. This survey is by far the most complete
and up-to-date information available today on commercial ALS. Additional
data on contact information, links and, in some cases, a short background
is given for firms involved in ALS (manufacturers, service providers, owners).
A summary of other non-commercial and research systems, mainly of NASA,
and respective links is presented. Finally, some other useful WEB links
are given. The developments in ALS have been very rapid the last 1¯2
years. This overview reflects these developments and describes rather completely
the current situation, thus, being useful for all persons involved in ALS
one way or another.
Baltsavias, E. P. (1999). A comparison between photogrammetry and
laser scanning. Isprs Journal of Photogrammetry and Remote Sensing,
V54, (N2-3): 83-94.
Keywords: photogrammetry airborne laser scanning
Synopsis: An overview paper of the differences between these two systems:
describes how they can be integrated. "ALS (can) 'see' objects smaller
then the footprint (small opening below vegetation, powerlines, etc…)"
Original Abstract: A comparison between data acquisition and processing
from passive optical sensors and airborne laser scanning is presented.
A short overview and the major differences between the two technologies
are outlined. Advantages and disadvantages with respect to various aspects
are discussed, like sensors, platforms, flight planning, data acquisition
conditions, imaging, object reflectance, automation, accuracy, flexibility
and maturity, production time and costs. A more detailed comparison is
presented with respect to DTM and DSM generation. Strengths of laser scanning
with respect to certain applications are outlined. Although airborne laser
scanning competes to a certain extent with photogrammetry and will replace
it in certain cases, the two technologies are fairly complementary and
their integration can lead to more accurate and complete products, and
open up new areas of application.
Barducci, A.; I. Pippi (2001). Analysis and rejection of systematic
disturbances in hyperspectral remotely sensed images of the Earth.
Applied Optics, V40, (N9): 1464-1477.
Keywords: Applied Physics/Condensed Matter/Materials Science ; Optics/Acoustics
Barnsley, M. J.; S. L. Barr (1997). Distinguishing urban land-use
categories in fine spatial resolution land-cover data using a graph-based,
structural pattern recognition system. Computers, Environment and
Urban Systems, 21, (3-4): 209-25.
Keywords: Cartography; Geographic information systems; Graph theory;
Image resolution; Pattern recognition; Remote sensing; Town and country
planning; Tree searching; Visual databases; Urban land use categories;
Spatial resolution; Land cover data; Graph-based structural pattern recognition
system; xrag; Extended Relational Attribute Graph; Remotely-sensed images;
Land use maps; Ordnance Survey digital map data; Morphological properties;
Spatial relations; Graph searching; Graph similarity measures ; Land cover
pattern
Original Abstract: This paper presents a preliminary test of a graph-based,
structural pattern recognition system-known as XRAG (eXtended Relational
Attribute Graph)-that might be used to infer broad categories of urban
land-use from very fine spatial resolution, remotely-sensed images. XRAG
allows the structural properties of and relations between, discrete land-cover
parcels to be analyzed and interpreted. Although the eventual aim is to
derive land-use maps directly from remotely-sensed images, this paper employs
Ordnance Survey 1:1,250 scale digital map data to provide the initial land-cover
information. These data, free from the complex effects of mixed pixels,
misclassification, shadowing and occlusion associated with remotely-sensed
images, are used to examine the intrinsic separability of several different
categories of urban land-use based on the morphological properties of,
and the spatial relations between, their component land-cover parcels.
In future studies, the system will be tested on real images. The current
system also needs to be extended to incorporate graph searching algorithms
and graph similarity measures, so that it can be used not only to describe
the structural differences between sample areas of known land use, but
also to infer land use from the spatial pattern of land cover.
Barr, S.; M. Barnsley (2000). Reducing structural clutter in land
cover classifications of high spatial resolution remotely-sensed images
for urban land use mapping. Computers & Geosciences,
V26, (N4): 433-449.
Keywords: land use mapping mixed pixels
Synopsis: This paper discusses a method of reducing structural clutter
or "noise", i.e. mixed pixels, shadowing, and occlusion, with a reflexive
mapping procedure in which noisy pixels are re-labeled. This is a "region-based"
method that considers area and adjacency. This method appears to work better
than "majority filtering" methods.
Original Abstract: A new generation of very high spatial resolution
(1¯5 m) satellite sensors is due to be launched within the next five
years. Among other things, images acquired by these sensors offer considerable
potential for the derivation of information on urban land use. It has been
suggested that this can be achieved via a two-stage process involving (i)
a standard (per-pixel) multispectral classification algorithm to identify
the principal land cover parcels present in the observed scene and (ii)
the application of structural pattern-recognition techniques to infer land
use from the morphological properties of these parcels and the spatial
relations that exist between them. It is implicit in this approach that
the initial classification is of sufficient accuracy to allow land use
to be inferred from these structural properties and relations. This assumption
is investigated using airborne multispectral image data with a nominal
spatial resolution of 2 m. It is shown that these data allow many features
of interest in urban areas to be identified and delineated, but that they
contain a significant amount of unwanted spatial detail (or `scene noise').
The latter results in structural `clutter' in the initial land cover classification,
which limits the potential to infer land use in the second stage of the
data-processing chain. To address this issue, a simple, region-based, reflexive-mapping
procedure is developed. This operates at the parcel (cf. pixel) level.
The procedure is successful at removing much of the structural clutter,
and performs well in comparison with traditional majority filtering; however,
the inference of urban land use from the resulting data remains problematic.
Basoz, N.; S. A. King; A. S. Kiremidjian; K. H. Law, (1996). Utilisation
of GIS and network analysis for earthquake damage assessment of transportation
systems. International Conference on Information Technology in Civil
and Structural Engineering Design. Information Processing in Civil and
Structural Engineering Design Glasgow, UK 14-16 Aug. 1996
Edinburgh, UK Civil-Comp Press, pp.151-60.
Keywords: Civil engineering computing; Decision support systems;
Earthquakes; Geographic information systems; Transportation; gis; Network
analysis; Earthquake damage assessment; Transportation systems; Seismic
risk; Transportation structures; Emergency operations; Computational methodology;
Performance evaluation ; Network analysis methodology
Original abstract: The potential seismic risk to the infrastructure
of a region and the consequences of failure have been recognized as important
issues within the last few decades. Damages to transportation structures
during an earthquake often result in severe disruptions to the transportation
systems of the region, causing major delays and affecting emergency operations.
The paper describes a computational methodology for evaluating the performance
of a transportation system in an area affected by an earthquake, utilizing
modern computational tools and network analysis methodology.
Baumgartner, A.; C. T. Steger; H. Mayer; W. Eckstein, (1997). Semantic
objects and context for finding roads. Integrating Photogrammetric
Techniques with Scene Analysis and Machine Vision III Orlando, FL, USA
21-23 April 1997
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.98-109.
Keywords: Feature extraction; Geographic information systems; Image
recognition; Image texture; Object recognition; Photogrammetry; Remote
sensing; Semantic objects; Multiresolution approach; Automatic road extraction;
Digital aerial imagery; Network; Intersections; Rural areas; Forest areas;
Urban areas; Texture analysis; Edge extraction; Line extraction; Reduced
resolution; Roadsides ; Quadrilaterals
Original abstract: This paper presents a multiresolution approach for
automatic extraction of roads from digital aerial imagery. Roads are modeled
as a network of intersections and links between the intersections. For
different context regions, i.e., rural, forest, and urban areas, the model
describes different relations between background objects, e.g., buildings
or trees, and semantic road objects, e.g., road-parts, road-segments, road-links,
and intersections. The classification of the image into context regions
is done by texture analysis. The approach to detect roads is based on the
extraction of edges in a high resolution image and the extraction of lines
in an image of reduced resolution. Using both resolution levels and explicit
knowledge about roads, hypotheses for roadsides are generated. The roadsides
are used to construct quadrilaterals representing road-parts and polygons
representing intersections. Neighboring road-parts are chained to road-segments.
Road-links, i.e., the roads between two intersections, are built by grouping
of road-segments and closing of gaps between road-segments. Road-links
are constructed using knowledge about context.
Bejleri, I.; A. Lyons; P. Zwick, (2000). Environmental Screening
Analysis Tool for transportation projects. URISA 2000 Annual Conference
and Exposition Proceedings of 37th Annual Conference of the Urban and Regional
Information Systems Association Orlando, FL, USA 19-23 Aug. 2000
Park Ridge, IL, USA Urban & Regional Inf. Syst. Assoc, pp.608-17.
Keywords: Cartography; Environmental science computing; Geographic
information systems; Town and country planning; Transportation; Environmental
Screening Analysis Tool; Transportation planning; University of Florida;
esat; Florida Department of Transportation; ArcView; Spatial analyses;
html; Maps; Geographic information system ; Urban planning
Original abstract: In an effort to improve and streamline transportation
planning, an inter-agency taskforce led by the Florida Governor's Office
developed guidelines for a process to identify environmentally sensitive
transportation projects in 1999. The objective of the methodology is to
identify the major impact issues of transportation projects early enough
in the planning process so that they may be discussed and resolved by the
appropriate agencies before additional resources are invested into planning
and implementation. Subsequently, the University of Florida Geoplan Center
was asked to develop the Environmental Screening Analysis Tool (ESAT) for
the Florida Department of Transportation to implement the screening guidelines.
Packaged as an ArcView extension, ESAT semi-automates project screening
by running a series of spatial analyses and generating HTML and written
outputs which present quantitative results and maps of potential impact
areas.
Bell, C.; W. Acevedo; J. T. Buchanan, (1995). Dynamic mapping of
urban regions: growth of the San Francisco/Sacramento region. URISA
Proceedings. Papers from the Annual Conference of the Urban and Regional
Information Systems Association Proceedings of 33rd Annual URISA Conference
San Antonio, MN, USA 16-20 July 1995
Washington, DC, USA Urban & Regional Inf. Syst. Assoc, pp.723-34.
Keywords: Computer animation; Geographic information systems; History;
Time series; Town and country planning; Transportation; Visual databases;
Dynamic mapping; Urban regions; San Francisco/Sacramento region; Large
metropolitan regions; Geographic information system; Urbanization; Urban
region; Historical records; USGS topographic maps; Aerial photographs;
Landsat imagery; Urban spatial extent; Digital transportation data; Tabular
census data; Time series animation; Urban growth; Urbanized region; Temporal
spatial data ; Spatial patterns
Original abstract: A methodology has been developed to document the
tremendous growth which large metropolitan regions have experienced over
time. A geographic information system (GIS) was used to compile a database
of urbanization for the San Francisco/Sacramento urban region spanning
140 years. Historical records, USGS topographic maps, aerial photographs
and Landsat imagery were used to identify the urban spatial extent. Digital
transportation data and tabular census data were also incorporated into
the database to provide a more complete picture of changes occurring over
time. A time series animation of urban growth for the urbanized region
depicts the alarming growth patterns the area experienced between the mid
1800s and the 1990s. The same process is being used to document growth
in other urban regions, such as the Baltimore-Washington area. This innovative
use of temporal spatial data and animation focuses attention on the dramatic
increases in urban development and the spatial patterns that have developed
over time.
Bellagente, M.; P. Gamba; P. Savazzi, (1999). Fuzzy texture characterization
of urban environments by SAR data. IEEE 1999 International Geoscience
and Remote Sensing Symposium. IGARSS'99 Hamburg, Germany 28 June-2 July
1999
Piscataway, NJ, USA IEEE, pp.1232-4 vol.2.
Keywords: Geophysical signal processing; Geophysical techniques;
Image classification; Image texture; Radar imaging; Remote sensing by radar;
Synthetic aperture radar; Terrain mapping; Geophysical measurement technique;
Land surface; Radar remote sensing; Fuzzy texture characterization; Urban;
Town; City; sar; Texture-base approach; Co-occurrence measure; Wavelet
frame decomposition; Co-occurence matrix; Polarimetric SAR image; Radar
polarimetry; Los Angeles; California ; United States
Original abstract: The authors present a texture-base approach to the
classification of SAR images recorded over urban environments. In particular,
they explore the use of some co-occurrence measures and the wavelet frame
decomposition, to investigate if there is an advantage, and where, in using
these tools. They found that the correct classification rates are only
partially increased by using these additional information, with a slight
preference for texture analysis through the co-occurence matrix. These
considerations are validated by analyzing polarimetric SAR images recorded
over Los Angeles by the AIRSAR sensor.
Belov, M. L.; V. A. Gorodnichev; V. I. Kozintsev (1996). Remote Sensing
of Oil Films on the Sea Surface Using a Satellite-Based Lidar. Earth
Observation and Remote Sensing, V13, (N6): 919-927.
Keywords: Optics/Acoustics
Benedikisson, K.; H. Benedikisson; J. A. Benedikisson; J. R. Sveinsson,
(1998). An extension of parametric decision boundary feature extraction
(DBFE) for classification of hyperdimensional data. IGARSS '98. Sensing
and Managing the Environment. 1998 IEEE International Geoscience and Remote
Sensing. Symposium Proceedings. Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE., pp.2694-6 vol.5.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Multidimensional signal processing; Remote
sensing; Terrain mapping; Geophysical measurement technique; Land surface;
Optical imaging; Multispectral method; Hyperspectral imagery; Parametric
decision boundary feature extraction ; Hyperdimensional data
Original abstract: Decision boundary feature extraction (DBFE) estimates
the decision boundary between individual classes and uses it for feature
extraction. Because the DBFE relies on the estimate of the decision boundary,
it fails when not enough data are available. To overcome this problem,
it is suggested to increase the size of the training set by including random
data based on the estimated mean vectors and covariance matrices of the
classes in the original training set. In experiments, this approach shows
potential when very limited training data are available for some classes.
Benediktsson, J. A.; K. Arnason; A. Hjartarson; D. A. Landgrebes, (1996).
Classification and feature extraction with enhanced statistics.
IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.
Remote Sensing for a Sustainable Future (Cat. No.96CH35875) IGARSS '96.
Lincoln, NE, USA 27-31 May 1996
New York, NY, USA IEEE, pp.414-16 vol.1.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Infrared imaging; Optical information
processing; Remote sensing; Geophysical measurement technique; Terrain
mapping; Land surface; Optical imaging; IR imaging; Satellite remote sensing;
Image processing; Enhanced statistics; aviris; Spatial classifier; Pixel
classifier; Decision boundary feature extraction; Discriminant analysis;
Geological unit; Spectral properties ; Geology
Original abstract: Classification of AVIRIS data is considered with
respect to enhanced statistics. The performance of enhanced statistics
is investigated in terms of feature extraction for both pixel and spatial
classifiers. The feature extraction methods applied are decision boundary
feature extraction and discriminant analysis. The classification results
obtained by enhanced statistics are excellent and show the classifiers
to be able to distinguish between several geological units with very similar
spectral properties.
Benediktsson, J. A.; J. I. Ingimundarson; J. R. Sveinsson, (1997). Classification
and feature extraction of hyperdimensional data using LOOC covariance estimation.
IGARSS'97. 1997 International Geoscience and Remote Sensing Symposium.
Remote Sensing - A Scientific Vision for Sustainable Development (Cat.
No.97CH36042). Singapore 3-8 Aug. 1997
New York, NY, USA IEEE, pp.913-15 vol.2.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Remote sensing; Sensor fusion; Geophysical
measurement technique; Land surface; Terrain mapping; Multispectral remote
sensing; Hyperdimensional; looc; Image processing; Multidimensional signal
processing; Leave one out covariance; Covariance estimation; Decision boundary
feature extraction ; dbfe
Original abstract: New methods for processing of multisource and hyperdimensional
data are discussed both in terms of feature extraction and classification.
An extension to decision boundary feature extraction (DBFE) is proposed.
The extension is based on a recently developed covariance estimator, the
leave one out covariance (LOOC). The extended decision boundary method
is tested on a multisource remote sensing and geographic data set.
Benediktsson, J. A.; J. Sigurdsson; J. R. Sveinsson, (1998). Feature
extraction based on LOOC estimation. IGARSS '98. Sensing and Managing
the Environment. 1998 IEEE International Geoscience and Remote Sensing.
Symposium Proceedings Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.2053-5 vol.4.
Keywords: Covariance matrices; Edge detection; Estimation theory;
Feature extraction; Geophysical signal processing; Image classification;
Remote sensing; LOOC estimation; Decision boundary feature extraction;
dbfe; Covariance estimator; Leave one out covariance; LOOC-DBFE method
; Hyperdimensional remote data set
Original abstract: An extension to decision boundary feature extraction
(DBFE) is discussed. The extension is based on a covariance estimator,
the leave one out covariance (LOOC). The LOOC-DBFE method is tested on
a hyperdimensional remote data set and compared to original DBFE. The proposed
approach compares favorably with the original DBFE method, especially in
hyperdimensional cases.
Benediktsson, J. A.; J. R. Sveinsson, (1997). Classification of hyperdimensional
data using data fusion approaches. IGARSS'97. 1997 International Geoscience
and Remote Sensing Symposium. Remote Sensing - A Scientific Vision for
Sustainable Development (Cat. No.97CH36042) IGARSS'97. Symposium Proceedings.
Singapore 3-8 Aug. 1997
New York, NY, USA IEEE, pp.1669-71 vol.4.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Geophysics computing; Image classification; Neural nets; Remote
sensing; Sensor fusion; Geophysical measurement technique; Image processing;
Terrain mapping; Multispectral remote sensing; Hyperspectral remote sensing;
Hyperdimensional data; Data fusion; Statistical classification method;
Consensus; Consensus theoretic methods; Weighting; Combined classification;
Weights; Decision boundary theory; Preprocessing; Neural network ; Neural
net
Original abstract: Statistical classification methods based on consensus
from several data sources are considered with respect to classification
and feature extraction of hyperdimensional data. The consensus theoretic
methods need weighting mechanisms to control the influence of each data
source in the combined classification. The weights are optimized in order
to improve the combined classification accuracies. Decision boundary feature
extraction is considered as a preprocessing method in the data fusion.
Consensus theory optimized with neural networks outperforms all other methods
in terms of test accuracies in the experiments.
Benediktsson, J. A.; J. R. Sveinsson (1997). Feature extraction for
multisource data classification with artificial neural networks. International
Journal of Remote Sensing, 18, (4): 727-40.
Keywords: Feature extraction; Multilayer perceptrons; Pattern classification;
Remote sensing; Sensor fusion; Multisource data classification; Artificial
neural networks; Remote sensing data; Geographic data; Principal component
analysis; pca; Discriminant analysis; Decision boundary feature extraction
method ; Multilayer neural networks
Original Abstract: Classification of multisource remote sensing and
geographic data by neural networks is discussed with respect to feature
extraction. Several feature extraction methods are reviewed, including
principal component analysis, discriminant analysis, and the recently proposed
decision boundary feature extraction method. The feature extraction methods
are then applied in experiments in conjunction with classification by multilayer
neural networks. The decision boundary feature extraction method shows
excellent performance in the experiments.
Benediktsson, J. A.; J. R. Sveinsson; K. Arnason (1995). Classification
and Feature Extraction of Aviris Data. Ieee Transactions on Geoscience
and Remote Sensing, V33, (N5): 1194-1205.
Keywords: feature extraction AVIRIS, feature classification
Benedirk, J.; Y. Nishiwaki, (1998). A fuzzy clustering application
to land use classification in satellite images. Fuzzy Logic and Intelligent
Technologies for Nuclear Science and Industry. Proceedings of the 3rd International
FLINS Workshop Antwerp, Belgium 14-16 Sept. 1998
Singapore World Scientific, pp.368-75.
Keywords: Fuzzy set theory; Image classification; Matrix algebra;
Pattern clustering; Remote sensing; Fuzzy clustering; Land use classification;
Satellite images; Nuclear safeguards; Ambiguity; Vagueness; Unsupervised
clustering; LANDSAT TM image; Urban area ; Euclidean-distance-based fuzziness
measure
Original abstract: In nuclear safeguards the pictures taken by satellite
may be used as an important means to identify doubtful places on land.
The paper describes a preliminary test for that purpose. With the advancement
of satellite technology, uncertainty due to low geometrical and spectral
resolution is diminishing. Different kinds of uncertainty are present,
however, while obtaining information on land use classes. Both the ambiguity
and the vagueness of drawing a line between two geographical regions are
addressed by fuzzy sets. Unsupervised clustering is performed on a LANDSAT
TM image of Vienna, Austria, the results being used as a measure of fuzziness
on the data. The degree of vagueness inherent to the subjective evaluation
of geographical terms, such as *urban area', is determined by a Euclidean-distance-based
measure of fuzziness.
Benitz, G. R. (1997). High-definition vector imaging. Lincoln
Laboratory Journal, 10, (2): 147-70.
Keywords: Airborne radar; Array signal processing; Feature extraction;
Image reconstruction; Image resolution; Maximum likelihood detection; Millimetre
wave imaging; Radar clutter; Radar imaging; Radar target recognition; Remote
sensing by radar; Speckle; Synthetic aperture radar; High-definition vector
imaging; Data-adaptive approach; Synthetic-aperture radar; Radar image
reconstruction; Superresolution techniques; Target recognition; uhf sar;
Millimeter-wave SAR; Target identification; Radar imagery; Two-dimensional
minimum-variance techniques; Maximum-likelihood algorithm; Capon algorithm;
Two-dimensional MUSIC algorithm; Multiple signal classification; Wideband
rail SAR measurements; SAR reflector measurements; Resolution improvement;
Clutter rejection; Airborne millimeter-wave SAR data; Speckle reduction;
HDVI vector aspect; Nonpointlike scattering models; Feature detection;
Vector image; Airborne UHF radar; Broadside flash model; Data information
; Adaptive beamforming
Original Abstract: High-definition vector imaging (HDVI) is a data-adaptive
approach to synthetic-aperture radar (SAR) image reconstruction based on
superresolution techniques originally developed for passive sensor arrays.
The goals are to produce more informative, higher-resolution imagery for
improving target recognition with UHF and millimeter-wave SAR and to aid
the image analyst in identifying targets in radar imagery. Algorithms presented
here include two-dimensional minimum-variance techniques based on the maximum-likelihood
method (Capon) algorithm and a two-dimensional version of the MUSIC (multiple
signal classification) algorithm. Simulations are used to compare processing
techniques and the results of wideband rail SAR measurements of reflectors
in foliage, demonstrating resolution improvement and clutter rejection
are presented. Results with airborne millimeter-wave SAR data demonstrate
improved resolution and speckle reduction. We also discuss the vector aspect
of HDVI, i.e., the incorporation of non-pointlike scattering models to
enable feature detection. An example of a vector image is presented for
data from an airborne UHF radar, using the broadside flash model to reveal
greater information in the data.
Berardino, P.; A. Borgia; G. Fornaro; R. Lanari; E. Sansosti; M. Tesauro,
(2000). Anticline growing beneath the urban area of Catania (Italy)
measured by SAR interferometry. IGARSS 2000. IEEE 2000 International
Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet:
The Role of Remote Sensing in Managing the Environment. Proceedings (Cat.
No.00CH37120) Honolulu, HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, pp.2218-20 vol.5.
Keywords: Geodesy; Geophysical techniques; Remote sensing by radar;
Synthetic aperture radar; Tectonics; Topography (Earth); Volcanology; Italy;
Sicily; Vertical motion; Uplift; Volcano; Anticline; Growth; Land surface;
Topography; Urban area; Catania; Town; SAR interferometry; InSAR; Radar
observations; Differential SAR interferometry; Rise; Basal anticline; Outward
thrusting; Basal decollement; Etna; Spreading process ; Geophysical measurement
technique
Original abstract: The authors applied the differential SAR interferometry
technique to detect and measure the rise of a basal anticline beneath the
urban area of Catania (Italy) which originates from outward thrusting above
the basal decollement of Etna volcano. This phenomenon, coupled with the
already documented active extension of the volcano summit area, demonstrates
the occurrence of the active volcanic spreading process.
Bernstein, R.; V. Di Gesu (1999). A combined analysis to extract
objects in remote sensing images. Pattern Recognition Letters,
20, (11-13): 1407-14.
Keywords: Feature extraction; Mathematical morphology; Object recognition;
Remote sensing; Statistical analysis; Remote sensing images; Shape recognition;
Resolution power ; Structural information
Original Abstract: This paper describes an object recognition system
to extract shape information from remote sensing images. One of the goals
is to determine if towers and power lines can be seen on one-meter imagery
and how much ground conditions can influence the resolution power of the
recognition algorithms. To this end, an integrated analysis system has
been implemented inside the remote sensing imaging system. The methodology
consists in the combination of statistical and structural information.
It has been tested on real images and it can be integrated in an automatic
system for the assessment of post storm damage.
Bernstein, R.; M. Oristaglio; D. E. Miller; J. Haldorsen (2000). Imaging
radar maps underground objects in 3-D. IEEE Computer Applications
in Power, 13, (3): 20-4.
Keywords: Buried object detection; Electric conduits; Radar imaging;
Underground cables; Imaging radar; Underground objects mapping; Underground
electric lines; Underground gas lines; Underground communication lines;
Maintenance; Conduits location; Cables location; Subsurface networks mapping;
Ground-penetrating imaging radar; Three-dimensional images; Schlumberger
Corporation; Electric Power Research Institute; Gas Research Institute;
Underground imaging system; Maintenance costs reduction; Utility operating
costs reduction; New York City; San Diego; Urban areas; Mapping system;
3 m ; 10 feet
Original Abstract: City streets cover a complex array of underground
electric, gas, and communication lines. Effective maintenance, expansion,
and new installation of these networks require accurate information regarding
the location of the conduits, cables, and other structures that lie beneath
the surface. Underground maps, if they exist, are often inaccurate, incomplete,
or out of date, and attempts to find underground lines or obstacles using
metal locators often prove disappointing. To help companies create accurate
maps of subsurface networks, researchers have developed a new ground-penetrating
imaging radar (GPIR) system that creates sharp, three-dimensional (3-D)
images of underground lines and objects. Schlumberger Corporation, in conjunction
with the Electric Power Research Institute (EPRI) and the Gas Research
Institute, has developed a GPIR system that detects, locates, and produces
3D maps of underground features. The new underground imaging system holds
the potential to reduce utility operating and maintenance costs by avoiding
unneeded excavation and by reducing incidences of costly damage such as
ruptured gas lines. Field demonstrations in New York City, San Diego, and
other utility locations have proven the ability of the new mapping system
to create accurate images of objects in crowded urban areas at depths as
great as 10 ft (3 m).
Berthelot, C.; T. Scullion; R. Gerbrandt; L. Safronetz (2001). Ground-penetrating
radar for cold in-place recycled road systems. Journal of Transportation
Engineering-Asce, V127, (N4): 269-274.
Keywords:
Bessettes, V.; J. Desachy, (1998). Extraction and classification
of urban areas on SPOT images. IGARSS '98. Sensing and Managing the
Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium
Proceedings. (Cat. No.98CH36174)Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.2583-6 vol.5.
Keywords: T. I. Stein
Original abstract: The study of urban area is an important problem
in image interpretation. It is interesting to be able to analyse town development,
to make streets maps automatically or just, to mask urban areas in satellite
images. The objective of this study is to extract urban areas from remote
sensing images and to make a classification of these areas. The proposed
method combines different types of operators in order to improve the final
detection. At first, the authors separate urban areas from the other type
of regions (vegetation, rivers...). Then these urban areas are segmented
according to the thoroughfares to obtain urban districts. Finally, the
authors define a measure of urban density. This study is performed by IRIT
and has been partially funded by CNES agency in the frame of the CNES program
on studies and research on automatic analysis and interpretation of SPOT
images.
Bessettes, V.; J. Desachy, (1998). Using SPOT images for urban area
classification. Image and Signal Processing for Remote Sensing IV Barcelona,
Spain 21-23 Sept. 1998
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.188-200.
Keywords: Feature extraction; Geography; Image classification; Mathematical
operators; Remote sensing; Urban areas detection; Classification; SPOT
images; Co-operative operators; Image interpretation; Town development;
Satellite images; Extraction; Remote sensing images ; Urban density
Original abstract: The urban areas represent a vast subject in image
interpretation. It is interesting to be able to analyze town development,
to make street maps automatically or just, to mask the urban areas in satellite
images. The objective of this study is to extract urban areas from remote
sensing images and to make a classification of these areas. The proposed
method combines different types of operators. At first, we define automatically
a mask of the urban areas by combining a classification algorithm with
edge extraction algorithms. Then these urban areas are segmented according
to the streets, railways and rivers to obtain urban districts. Finally,
we define a measure of urban density. In this paper, we focus on the urban
extraction algorithm and the urban segmentation process. This study is
performed by IRIT and has been partially funded by CNES agency.
Bessettes, V.; J. Desachy, (1997). Urban areas detection and classification
on SPOT images using co-operative operators. Image Processing, Signal
Processing, and Synthetic Aperture Radar for Remote Sensing London, UK
22-26 Sept. 1997
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.320-31.
Keywords: Feature extraction; Geography; Image classification; Mathematical
operators; Remote sensing; Urban areas detection; Classification; SPOT
images; Co-operative operators; Image interpretation; Town development;
Satellite images; Extraction; Remote sensing images ; Urban density
Original abstract: The study of urban areas is an important problem
in image interpretation. It is interesting to be able to analyze town development
on satellite images or to mask urban areas automatically. The method we
present in this paper consists in the extraction of urban areas from remote
sensing images and the classification of these areas. We separate the urban
areas from the other types of regions. Then we classify them according
to a measure of the urban density. The algorithms we use, combine different
types of operators in order to improve the final classification.
Bessettes, V.; J. Desachy; E. Cubero-Castan, (1996). Applying co-operative
operators for urban area detection using SPOT imagery. Image and Signal
Processing for Remote Sensing III Taormina, Italy 23-25 Sept. 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.106-17.
Keywords: Feature extraction; Image classification; Image segmentation;
Remote sensing; Co-operative operators; Urban area detection; SPOT imagery;
Image interpretation; Town development; Satellite images; Urban areas;
Remote sensing images; Detection algorithm; irit; CNES program ; Automatic
analysis
Original abstract: The study of urban area is an important problem
in image interpretation. It is interesting to be able to analyse town development
on satellite images or to mask urban areas. The objective of this study
is to extract urban areas from remote sensing images and to make a classification
of these areas. The detection algorithm combines different types of operators
in order to improve the final detection. We separate urban areas from the
other type of regions (vegetation, rivers etc.). Then the urban areas are
classified into various under-classes (dense urban areas, suburbs etc.).
This study has been performed by IRIT in the frame of the CNES program
on studies and research on automatic analysis and interpretation of SPOT
images.
Bessettes, V.; J. Desachy; M. J. Lefevre, (2000). Use of directional
variance for urban area analysis on simulated Spot 5 images. IGARSS
2000. IEEE 2000 International Geoscience and Remote Sensing Symposium.
Taking the Pulse of the Planet: The Role of Remote Sensing in Managing
the Environment. Proceedings (Cat. No.00CH37120) Honolulu, HI, USA 24-28
July 2000
Piscataway, NJ, USA IEEE, pp.2890-2 vol.7.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Image texture; Remote sensing; Terrain
mapping; Geophysical measurement technique; Land surface; Optical imaging;
Satellite remote sensing; spot 5; Urban area; Town; City; Directional variance;
Urban area analysis; High resolution; Panchromatic mode; Panchromatic image;
Road; Textural feature extraction; Image processing; Segmentation; Edge
detection ; Detection algorithm
Original abstract: CNES is due to launch the SPOT 5 satellite during
2001. One of its particularities, compared to the previous generations
of SPOT satellites, will be its high resolution in panchromatic mode (5m
and Supermode 2.5m). Before launch, CNES has to validate the choices made
for the sensors with the use of simulated images. The authors's study has
been made as part of the use of SPOT 5 panchromatic images for urban area
analysis. The aim of the study is to extract urban areas and roads from
panchromatic simulated SPOT 5 images. The proposed method combines a textural
feature extraction with segmentation by edge detection in order to improve
the final classification. At first, they define a textural feature based
on the directional variance of the image. Then they use classification
algorithms on this feature, combined with an edge detection operator, to
extract the urban areas and the roads from the image.
Betti, A.; M. Barni; A. Mecocci, (1997). Using a wavelet-based fractal
feature to improve texture discrimination on SAR images. Proceedings.
International Conference on Image Processing (Cat. No.97CB36144) Santa
Barbara, CA, USA 26-29 Oct. 1997
Los Alamitos, CA, USA IEEE Comput. Soc
Proceedings., pp.251-4 vol.1.
Keywords: Electromagnetic wave polarisation; Feature extraction;
Fractals; Fuzzy systems; Image representation; Image segmentation; Image
texture; Radar applications; Radar imaging; Remote sensing by radar; Synthetic
aperture radar; Wavelet transforms; Texture discrimination; SAR images;
Wavelet-based fractal feature; Cover classes discrimination; Remote sensing
image segmentation; Clustering techniques; Pyramid-based methods; Performance;
Global algorithms; Single polarization synthetic aperture radar; SAR data;
Mono-band images; Wavelet-based fuzzy clustering algorithm; Texture image;
Fractal model; Wavelet representation ; X-SAR images
Original abstract: Clustering is commonly used in remote sensing image
segmentation. Among the clustering techniques, pyramid-based methods generally
provide better performance in discriminating among different cover classes
if compared to global algorithms. When applied to single polarization synthetic
aperture radar (SAR) data, though, such algorithms suffer from misinterpretation
problems due to the mono-band nature of the images produced by these sensors.
In this case an important feature to improve the segmentation is texture.
This paper describes a wavelet-based fuzzy clustering algorithm which receives
as input both the remotely sensed image and a texture image based on a
fractal model, derived from the wavelet representation itself. The algorithm
has been tested on X-SAR images, and the results demonstrate its potential
usefulness.
Bhaskar, S.; B. Datt, (2000). Sub-pixel analysis of urban surface
materials. A case study of Perth, W. Australia. IGARSS 2000. IEEE 2000
International Geoscience and Remote Sensing Symposium. Taking the Pulse
of the Planet: The Role of Remote Sensing in Managing the Environment.
Proceedings (Cat. No.00CH37120)Honolulu, HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, pp.1535-7 vol.4.
Keywords: Geophysical techniques; Remote sensing; Terrain mapping;
Geophysical measurement technique; Land surface; Multispectral remote sensing;
Hyperspectral remote sensing; Visible; ir; Infrared; Town; City; Urban
scene; Perth; Australia; Subpixel analysis; Urban surface material; Spectral
library; Spectral unmixing; Geo-referenced map ; Surface materials
Original abstract: Hyperspectral analysis of urban areas has the potential
to deliver a cost-effective alternative to urban planning and management.
This paper describes the compilation of a spectral library of urban surface
materials which was used for calibrating an airborne hyperspectral image
of a selected area in Perth, Western Australia. Spectral unmixing was performed
to decompose this image into abundance of individual surface materials.
A geo-referenced map showing the distribution of surface materials was
generated.
Bhaumik, D.; N. L. Faust; D. Estrada; J. Linares, (1997). Three-dimensional
urban GIS for Atlanta. Modeling, Simulation, and Visualization of Sensory
Response for Defense Applications Orlando, FL, USA 22-23 April 1997
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.115-24.
Keywords: Cartography; Data visualisation; Geographic information
systems; Image resolution; Message passing; Multimedia computing; Remote
sensing; Virtual reality; Urban GIS; Virtual 3D geographic information
system; GA Atlanta, USA; Prototype system; Interactive tool; Spatial data
exploration; High-density urban environment; Terrain elevation; Imagery;
GIS layers; Natural features; Man-made features; 1996 Olympic Games; Olympic
Village; Georgia Tech; Detailed 3D databases; Downtown area; Visualization
software; Query functions; Analysis functions; Productivity ; Multimedia
Original abstract: Georgia Tech has developed a prototype system for
the demonstration of the concepts of a virtual 3D geographic information
system (GIS) in an urban environment. The virtual GIS integrates the technologies
of GIS, remote sensing and visualization to provide an interactive tool
for the exploration of spatial data. A high-density urban environment with
terrain elevation, imagery, GIS layers and 3D natural and man-made features
is a stressing test for the integration potential of such a virtual 3D
GIS. In preparation for the 1996 Olympic Games, Georgia Tech developed
two highly detailed 3D databases over parts of Atlanta. A 2.5-m database
was used to depict the downtown Atlanta area, with much higher resolution
imagery being used for photo-texture of individual Atlanta buildings. Less
than 1-m imagery data was used to show a very accurate map of Georgia Tech
and the 1996 Olympic Village. The Georgia Tech-developed visualization
software was integrated via message passing with a traditional GIS package
so that all commonly-used GIS query and analysis functions could be applied
within the 3D environment. This project demonstrates the versatility and
productivity that can be accomplished by operating GIS functions within
a virtual GIS and multimedia framework.
Bickel, D. L.; W. H. Hensley; D. A. Yocky, (1997). The effect of
scattering from buildings on interferometric SAR measurements. IGARSS'97.
1997 International Geoscience and Remote Sensing Symposium. Remote Sensing
- A Scientific Vision for Sustainable Development (Cat. No.97CH36042) Singapore
3-8 Aug. 1997
New York, NY, USA IEEE, pp.1545-7 vol.4.
Keywords: Geodesy; Geophysical techniques; Height measurement; Remote
sensing by radar; Synthetic aperture radar; Topography (Earth); Geophysical
measurement technique; Land surface topography; Terrain mapping; Urban
area; Buildings; Building; City; Town; Radar remote sensing; Spaceborne
radar; Radar imaging; Interferometric SAR; ifsar; InSAR; Elevation model;
Scattering mechanism ; Coherence
Original abstract: The determination of elevation models of buildings
using interferometric synthetic aperture radar (IFSAR) is an important
area of active research. The focus of this paper is on some of the unique
scattering mechanisms that occur with buildings and how they affect the
IFSAR height measurement and the coherence. The authors show by theory
and examples that the various data products obtained from IFSAR can be
used to aid in interpreting building height results. They also present
a method that they have used successfully in mapping buildings in Washington
D.C.
Blacknell, D.; R. J. A. Tough (1997). Clutter discrimination in polarimetric
and interferometric synthetic aperture radar imagery. Journal of
Physics D (Applied Physics), 30, (4): 551-66.
Keywords: Ecology; Feature extraction; Pollution measurement; Radar
clutter; Radar imaging; Radar polarimetry; Remote sensing by radar; Synthetic
aperture radar; Clutter discrimination; Interferometric synthetic aperture
radar imagery; Polarimetric synthetic aperture radar imagery; Vegetation;
Environmental effects; Damping; Sea surface; Pollutants; Maritime military
targets; Land-based targets; Background clutter; Localized clutter features;
Multiple-channel SAR systems ; Statistical characteristics
Original Abstract: Many synthetic aperture radar (SAR) images contain
extended regions of apparently homogeneous clutter arising from areas of
vegetation or uniformly driven expanses of water. These regions may contain
localized clutter variations resulting from the presence of features of
ecological interest, such as changes in vegetation density due to environmental
effects or damping of the sea surface by pollutants. Alternatively such
clutter variations may be due to the signatures of partially concealed
land-based or maritime military targets. It is thus of interest to develop
techniques which can discriminate localized clutter features from the background
clutter. Multiple-channel SAR systems can provide several images of a scene
of this type which contain complementary sets of information. These can
be combined to generate a single enhanced image of the scene in which it
is possible to discriminate more effectively among the features it contains.
In this paper a unified discussion of multi-channel enhancement techniques,
which make use of varying degrees of knowledge regarding the statistical
characteristics of the image features, is presented.
Blonda, P.; A. Bennardo; G. Pasquariello; G. Satalino; V. la Forgia,
(1996). Application of the fuzzy Kohonen clustering network to remote
sensed data processing. Applications of Fuzzy Logic Technology III
Orlando, FL, USA 10-12 April 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.119-29.
Keywords: Backpropagation; Feature extraction; Fuzzy neural nets;
Image classification; Remote sensing; Self-organising feature maps; Fuzzy
Kohonen clustering network; Remote sensed data processing; Classification
experiments; Multi-modular neural classification system; Labelling; Unsupervised
module; Supervised module; Backpropagation learning rule; Neural net performance;
Kohonen self organizing map neural network ; Complex data pre-processing
Original abstract: The effectiveness of the fuzzy Kohonen clustering
network (FKCN) has been explored in two classification experiments of remote
sensed data. The FKCN has been introduced in a multi-modular neural classification
system for feature extraction before labelling. The unsupervised module
is connected in cascade with the next supervised module, based on the backpropagation
learning rule. The performance of the FKCN has been evaluated in comparison
with those of a conventional Kohonen self organizing map (SOM) neural network.
Experimental results have proved that the fuzzy clustering network can
be used for complex data pre-processing.
Blonda, P.; A. Bennardo; G. Satalino; G. Pasquariello (1996). Fuzzy
logic and neural techniques integration: an application to remotely sensed
data. Pattern Recognition Letters, 17, (13): 1343-8.
Keywords: Fuzzy logic; Fuzzy neural nets; Fuzzy set theory; Neural
net architecture; Pattern classification; Remote sensing; Unsupervised
learning; Remotely sensed data; Fuzzy neural networks; Fuzzification; Unsupervised
fuzzy architecture; Feature extraction ; Fuzzy min-max neural networks
Original Abstract: The paper reviews the most recent proposals on the
integration of fuzzy and neural networks techniques. First, it focuses
on the strategies developed and employed for the fuzzification of neural
network architectures. Then it applies an unsupervised fuzzy architecture
to the analysis of remotely sensed data and compares the results with those
obtained by means of a conventional neural model.
Blonda, P.; A. M. Bognani; G. Ria; G. Satalino; A. Baraldi, (1998).
Neuro-fuzzy analysis of remote sensed Antarctic data. New Trends
in Fuzzy Logic II. Proceedings of the Second Italian Workshop on Fuzzy
Logic Proceedings of the WLF 97 - Italian Workshop on Fuzzy Logic Bari,
Italy 19-20 March 1997
Singapore World Scientific, pp.284-91.
Keywords: ART neural nets; Data analysis; Feature extraction; Fuzzy
neural nets; Neural net architecture; Remote sensing; Self-organising feature
maps; Neuro-fuzzy architecture; Fully self-Organizing Simplified Adaptive
Resonance Theory; Remote sensed Antarctic data; Classification experiment;
Fuzzy set memberships; Weights updating; Disjointed subnets; fosart; Faster
adaptivity; Vigilance test ; Neuron proliferation.
Original abstract: A new neuro-fuzzy architecture, the Fully self-Organizing
Simplified Adaptive Resonance Theory (FOSART), has been applied to the
analysis of remote sensed Antarctic data in a classification experiment.
FOSART employs fuzzy set memberships in the weights updating rule; it applies
an ART-based vigilance test to control neuron proliferation and takes advantage
of the fact that it employs a new version of the Competitive Hebbian Rule
to dynamically generate and remove synaptic links between neurons, as well
as neurons. FOSART can develop disjointed subnets. The results obtained
with FOSART have been compared with those obtained with Fuzzy Learning
Vector Quantization (FLVQ), and Self Organizing Feature Map (SOM) networks.
The finding suggests that FOSART performances are lower, at convergence,
than those of FLVQ and SOM, even if it shows a faster adaptivity to the
input data structure, due to its topological and on-line characteristics.
Blonda, P.; V. Laforgia; G. Pasquariello; G. Satalino (1996). Feature
Extraction and Pattern Classification of Remote Sensing Data by a Modular
Neural System. Optical Engineering, V35, (N2): 536-542.
Keywords: Applied Physics/Condensed Matter/Materials Science ; Optics/Acoustics
Bocco, G.; R. Sanchez; H. Riemann (1995). GIS affects flood planning
efforts. GIS World, 8, (2): 58-60.
Keywords: Disasters; Geographic information systems; Geophysical
catastrophes; Public administration; Remote sensing; Town and country planning;
Flood planning efforts; gis; Natural disaster; Developing countries; Contingency
planning; January 1993 floods; Tijuana; Mexico; Remote sensing technologies;
Spatial issues; Environmental issues; Urban matters; Temporal resolution;
Spatial resolution ; Aerial photographs
Original Abstract: Floods are the most frequent type of natural disaster,
especially in developing countries. Few results have been achieved to prevent
such catastrophes, emphasizing the vulnerability of the world's societies,
particularly the poorer ones, to natural disasters and the urgent need
to speed up contingency planning to control and reduce their impact. A
GIS was established during the January 1993 floods in Tijuana, Mexico,
to assess their effects and to contribute to more detailed contingency
planning efforts. GIS and remote sensing technologies have been used extensively
during the last five years to approach environmental and spatial issues
related to urban matters. The Tijuana study was based on that approach.
Because of the spatial and temporal resolutions required for this type
of research, aerial photographs were used rather than satellite images.
Boehner, C.; M. A. Esposito, (1996). Optical and radar merge: application
on Firenze's urban environment. Geographical Information from Research
to Application Through Cooperation. Second Joint European Conference and
Exhibition Proceedings of Joint European Conference on Geographical Information
Barcelona, Spain 27-29 March 1996
Amsterdam, Netherlands IOS Press, pp.679-82 vol.1.
Keywords: Geographic information systems; History; Remote sensing
by radar; Town and country planning; Visual databases; Urban environment;
European towns; Firenze; Urban planning; Monuments; Ancient squares; Bridges;
Remote sensing; Construction materials; Optical remotely sensed data; Resolution;
Remotely sensed radar data; Ground truth control data; End-user oriented
applicability; Databases ; Geographic information system
Original abstract: Historical European towns such as Firenze have a
very mixed urban tissue characterised by the presence of monuments, ancient
squares and bridges. The project will detect and classify by remote sensing
the construction materials forming the urban environment. The available
optical remotely sensed data lacks resolution and thematic information
which is needed for urban scale applications. The question: is remotely
sensed radar data being more precise in resolution, eventually combined
with optical RS data and to ground truth control data, able to offer more
satisfying results? If so, an automation methodology has to be developed
in order to offer end-user oriented applicability that allows to integrate
this data with already existing traditional databases.
Boerner, W. M.; J. S. Verdi, (1996). Recent advances in WISIP: wideband
interferometric sensing and imaging polarimetry. ISAP 1996. Proceedings
of the 1996 International Symposium on Antennas and Propagation Chiba,
Japan 24-27 Sept. 1996
Tokyo, Japan Inst. Electron. Inf. & Commun. Eng, pp.873-6 vol.3.
Keywords: Airborne radar; Disasters; Earthquakes; Environmental
factors; Feature extraction; Geophysical techniques; Global Positioning
System; Radar clutter; Radar imaging; Radar polarimetry; Radar target recognition;
Radiowave interferometry; Remote sensing by radar; Spaceborne radar; wisip;
Wideband interferometric sensing; Imaging polarimetry; Wide area environmental
monitoring; Terrestrial covers; Planetary covers; Dynamic optimal image
feature extraction; Target sections; Background clutter/speckle; Target
detection; Target recognition; Target identification; Vibrating target;
Interferometric POL-SAR imaging techniques; Polarimetric coregistered signature
sensing; Navigational electronic tools; dgps; NASA-JPL/AIR-SAR airborne
radar; nawc/p3-pol-sar; dlr-oph/do-pol-sar; ers-1/2; JERS-1 satellite radar;
Flood ; Earthquake
Original abstract: WISIP, wideband ( mu Hz-PHz) interferometric sensing
and imaging polarimetry, has become an indispensable tool in wide area
environmental monitoring of terrestrial and planetary covers. It allows
dynamic optimal image feature extraction of significant characteristics
of a desirable target and/or target sections with simultaneous suppression
of undesirable background clutter/speckle. WISIP may be adopted for the
detection, recognition and identification (DRI) of any stationary, moving
or vibrating target versus arbitrary stationary, dynamically changing or
moving geophysical/ecological environments. A comprehensive overview is
presented on how these modern high resolution/precision completely polarimetric
coregistered signature sensing and interferometric POL-SAR imaging techniques,
complemented by full integration of novel navigational electronic tools,
such as DGPS, will advance electromagnetic vector wave sensing and imaging
towards the limits of physical realizability. Various examples utilizing
NASA-JPL/AIR-SAR, NAWC/P3-POL-SAR, DLR-OPH/DO-POL-SAR airborne, ERS-1/2,
JERS-1 satellite and SIR-C/X-SAR shuttle imaging data sets dealing with
recent major flood and various earthquake surface deformation events as
well as other geo-environmental applications will be presented for demonstrating
the utility of WISIP.
Boesch, R., (1998). Framework for feature extraction of natural objects.
IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International
Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)
Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.2062-4 vol.4.
Keywords: Feature extraction; Geophysical signal processing; Image
recognition; Remote sensing; Natural objects; Image processing; Aerial
data; Satellite data ; Hypothesis-based approach
Original abstract: Feature extraction of natural objects is still a
very difficult task with todays image processing techniques. High resolution
aerial or satellite data can be acquired with reasonable effort, but accuracy
and correctness of available extraction methods are lagging behind. Instead
of propagating the ultimate segmentation algorithm, a hypothesis-based
approach is presented, which tries to combine established and new image
processing methods with each other. Similar to the varimax criteria of
principal component analysis, extraction methods should be as independent
as possible from each other.
Boile, M. P., (1998). Intermodal transportation network analysis-a
GIS application. Proceedings of 10th Mediterranean Electrotechnical
Conference - MELECON 2000 Lemesos, Cyprus 29-31 May 2000
Piscataway, NJ, USA IEEE, pp.660-3 vol.2.
Keywords: Geographic information systems; Planning; Rail traffic;
Transportation; Intermodal transportation network analysis; GIS application;
Integrated transportation system; Network equilibrium models; Transportation
planning; Future network activity; Traffic volumes; Travel costs; Intermodal
network representation; Geographic information system; Spatial data format;
Road network ; Transit network
Original abstract: A framework is presented which may be used to analyze
and evaluate intermodal networks. An intermodal network may be defined
as an integrated transportation system consisting of two or more modes.
Modes on intermodal networks are connected through facilities which allow
travelers and/or freight to transfer from one mode to another during a
trip from an origin to a destination. Network equilibrium models may be
used in the transportation planning field to make predictions regarding
future network activity in terms of traffic volumes and travel costs, to
evaluate alternative policies and to aid the decision making process in
terms of future transportation plans. To expedite and facilitate the effort
of detailed intermodal network representation and analysis, a network equilibrium
model is interfaced with a geographic information system (GIS). This interface
takes advantage of new technologies and sources of information on the physical
components of the network. It allows the user to store the results of the
proposed models in a GIS environment and display them in a spatial data
format.
Bolter, R.; F. Leberl, (2000). Detection and reconstruction of buildings
from multiple view interferometric SAR data. IGARSS 2000. IEEE 2000
International Geoscience and Remote Sensing Symposium. Taking the Pulse
of the Planet: The Role of Remote Sensing in Managing the Environment.
Proceedings (Cat. No.00CH37120) Honolulu, HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, pp.749-51 vol.2.
Keywords: InSAR data feature extraction, Interferometric SAR, Airborne
single pass IFSAR
Synopsis: The authors combine multiple views and multiple data sources
from INSAR sensors to extract buildings and other features with height
from other features in a dataset. Method works well except when a building
intersects the "shadow region" of another building.
Original abstract: Geometric reconstruction of human scale features
gets feasible from airborne single pass IFSAR sensors. IFSAR data is corrupted
by blur, speckle noise, and other view dependent effects as e.g., layover
and shadows. Especially in case of buildings, those phenomenological features
may also provide valuable information about the underlying structure. Combining
multiple views and multiple data types of the same scene the exploitation
of this information gets feasible. The authors use information from the
interferometric height and coherence data to separate regions containing
buildings from other objects in the scene. Shadow information from magnitude
images is then used to delimit the exact boundaries of the buildings further.
Rectangles are fit to the selected points and compared to ground truth
measurements manually derived from optical images.
Borak, J. S.; A. H. Strahler, (1996). Feature selection using decision
trees-an application for the MODIS land cover algorithm. IGARSS '96.
1996 International Geoscience and Remote Sensing Symposium. Remote Sensing
for a Sustainable Future (Cat. No.96CH35875) IGARSS Lincoln, NE, USA 27-31
May 1996
New York, NY, USA IEEE, pp.243-5 vol.1.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Remote sensing; Geophysical measurement
technique; Land surface; Terrain mapping; Feature selection; Decision trees;
Decision tree; MODIS land cover algorithm; Optical imaging; Visible; ir;
Global-scale land cover map; Classifier ; Proportional sampling strategy
Original abstract: One of the key issues involved in generating global-scale
land cover maps from remote sensing data is the discarding of useless or
redundant information. The decision tree offers a promising approach to
extraction of meaningful features from large measurement spaces. This research
examines the performance of several classifiers on subsets of features
produced via decision trees.
Borges, K. A. d.; S. Sahay (2000). GIS for the public sector: experiences
from the city of Belo Horizonte, Brazil. Information Infrastructure
and Policy, 6, (3): 139-55.
Keywords: Geographic information systems; Public administration;
Town and country planning; Traffic information systems; Transportation;
Public sector GIS; Belo Horizonte; Brazil; Brazilian municipal administration;
Urban geographic information system; Local government tradition; Education;
Health; Sanitation; Urban planning; Traffic; Technology acquisition; Team
formation phases; Geographic database; GIS technology usage; GIS implementation;
India; Future GIS projects ; Developing countries
Original Abstract: Belo Horizonte was one of the first Brazilian municipal
administrations to develop an urban geographic information system. Situated
within the local government tradition of local government, the development
and implementation of the geographic information system (GIS) commenced
in 1989 and has proceeded significantly to the extent that it has become
the most complete experience of its kind throughout Brazil, with applications
covering areas such as education, health, sanitation, urban planning, transportation
and traffic, among others. The article reflects on the experiences of this
GIS project, from the technology acquisition and team formation phases,
through the creation of the geographic database, to the development of
applications and dissemination among users. Current perspectives for the
continuing expansion of GIS technology usage in Belo Horizonte are also
presented. This "successful" experience of GIS implementation is contrasted
with some GIS projects in India to highlight probable areas of emphasis
in future GIS projects in developing countries.
Boryssenko, A.; V. I. Polishchuk, (1999). Earth near-surface passive
probing by natural pulsed electromagnetic field. 1999 International
Conference on Computational Electromagnetics and its Applications. Proceedings
(ICCEA'99) (IEEE Cat. No.99EX374) Beijing, China 1-4 Nov. 1999
Beijing, China Publising House of Electron. Ind, pp.529-32.
Keywords: Buried object detection; Remote sensing; Signal processing;
Statistical analysis; Transient analysis; Earth near-surface passive probing;
Natural pulsed electromagnetic field; Subsurface hidden objects; Statistical
variations; Statistical signal processing technique; Subsurface pipelines;
Cables; Hidden objects; Archeological objects; Passive probing technique
; Urban conditions
Original abstract: The results of experimental studies to detect and
locate subsurface hidden objects by near-surface determination of statistical
variations of the natural pulsed electromagnetic background field in the
0.1-50 kHz frequency range are presented. There are possibilities of passive
electromagnetic probing by use of the necessary electronic equipment and
statistical signal processing technique described. Some field results to
locate subsurface pipelines and cables and other hidden objects including
archeological ones are shown. The perspectives of this passive probing
technique for variety of applications in urban conditions are discussed.
Boudreau, E.; R. Huguenin; M. Karaska, (1996). Nonparametric classification
of subpixel materials in multispectral imagery. Algorithms for Multispectral
and Hyperspectral Imagery II Orlando, FL, USA 9-11 April 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.31-9.
Keywords: Agriculture; Feature extraction; Forestry; Image classification;
Nonparametric statistics; Remote sensing; Spectral analysis; Nonparametric
classification; Subpixel materials; Multispectral imagery; Applied analysis
spectral analytical process; aasap; Materials of interest; Environmental
correction; Signature derivation; Subpixel classification; Factor extraction;
Training set; Background estimation; Loblolly pine; Landsat TM scene; Crop
signature; Soil contamination; Wetlands species ; Lines of communication
Original abstract: An effective process for the automatic classification
of subpixel materials in multispectral imagery has been developed. The
applied analysis spectral analytical process (AASAP) isolates the contribution
of specific materials of interest (MOI) within mixed pixels. AASAP consists
of a suite of algorithms that perform environmental correction, signature
derivation, and subpixel classification. Atmospheric and sun angle correction
factors are extracted directly from imagery, allowing signatures produced
from a given image to be applied to other images. AASAP signature derivation
extracts a component of the pixel spectra that is most common to the training
set to produce a signature spectrum and nonparametric feature space. The
subpixel classifier applies a background estimation technique to a given
pixel under test to produce a residual. A detection occurs when the residual
falls within the signature feature space. AASAP was employed to detect
stands of Loblolly pine in a Landsat TM scene that contained a variety
of species of southern yellow pine. An independent field evaluation indicated
that 85% of the detections contained over 20% Loblolly, and that 91% of
the known Loblolly stands were detected. For another application, a crop
signature derived from a scene in Texas detected occurrences of the same
crop in scenes from Kansas and Mexico. AASAP has also been used to locate
subpixel occurrences of soil contamination, wetlands species, and lines
of communication.
Brecher, A.
Transportation Strategic Planning and Analysis Office
DOT/RSPA Volpe National Transportation Systems Center
55 Broadway, Cambridge, MA 02142
brecher@volpe.dot.gov
Summary of the DOT National Forum on Remote Sensing Applications
to Transportation
TS&T
http://scitech.dot.gov/reeng/sensmsrm/rmtsense/sbrsfsum.html
Brown, D. E.; J. Marin, (1995). Learning vector quantization for
road extraction from digital imagery. 1995 IEEE International Conference
on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century
(Cat. No.95CH3576-7) Vancouver, BC, Canada 22-25 Oct. 1995
New York, NY, USA IEEE, pp.1478-81 vol.2.
Keywords: Feature extraction; Image recognition; Remote sensing;
Vector quantisation; Learning vector quantization; Road extraction; Digital
imagery; Topographic information; Satellite imagery; Topographic feature
data; Road networks ; SPOT imagery
Original abstract: Many operations require the most accurate and complete
topographic information available. Typically map products cannot maintain
currency because of the rapid pace of development. Hence, there is an urgent
requirement to exploit satellite imagery to provide current topographic
feature data. Among the most important features needed are roads and, hence
we require automated procedures to rapidly identify road networks in imagery.
This paper describes the use of learning vector quantization to extract
roads from digital imagery. We provide results using data from SPOT imagery.
Bruce, L. M.; L. Jiang, (1999). Fast wavelet-based algorithms for
multiresolutional decomposition and feature extraction of hyperspectral
signatures. Algorithms for Multispectral and Hyperspectral Imagery
V Orlando, FL, USA 5-6 April 1999
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.72-81.
Keywords: Computational complexity; Feature extraction; Geophysical
signal processing; Image recognition; Image resolution; Remote sensing;
Wavelet transforms; Fast wavelet-based algorithms; Multiresolutional decomposition;
Hyperspectral signatures; Wavelet-based spectral fingerprint; Wavelet transform
modulus-maximus method; Computational expense ; Computational costs
Original abstract: Spectral features are often extracted from multispectral/hyperspectral
data using a multiresolutional decomposition known as the spectral fingerprint.
While the spectral fingerprint method has proven to be quite powerful,
it has also shown several shortcomings: (1) its implementation requires
multiple convolutions with Laplacian-of-Gaussian filters which are computationally
expensive, (2) it requires a truncation of the filter impulse response
which can cause spurious errors, and (3) it provides information about
the sizes and areas of radiance features but not the shapes. It is proposed
that a wavelet-based spectral fingerprint can overcome these shortcomings
while maintaining the advantages of the traditional method. In this study,
we investigate the use of the wavelet transform modulus-maximus method
to generate a wavelet-based spectral fingerprint. The computation of the
wavelet-based fingerprint is based on fast wavelet algorithms. The analysis
consists of two parts: (1) the computational expense of the new method
is compared with the computational costs of current methods, and (2) the
outputs of the wavelet-based methods are compared with those of current
methods to determine any practical differences in the resulting spectral
fingerprints.
Bruce, L. M.; J. Li, (1999). Enhancing hyperspectral data throughput
utilizing wavelet-based fingerprints. Image and Signal Processing for
Remote Sensing V Florence, Italy 22-24 Sept. 1999
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.218-27.
Keywords: Feature extraction; Geophysical signal processing; Image
enhancement; Image recognition; Remote sensing; Wavelet transforms; Hyperspectral
data throughput; Wavelet-based fingerprints; Multiresolution decompositions;
Spectral fingerprints; Spectral features; Multispectral hyperspectral data;
Wavelet-based algorithms; Computational expense; Computational costs; Hyperspectral
Digital Image Collection Experiment; HYDICE signature ; Average Euclidean
distance
Original abstract: Multiresolution decompositions known as spectral
fingerprints are often used to extract spectral features from multispectral
hyperspectral data. In this study, we investigate the use of wavelet-based
algorithms for generating spectral fingerprints. The wavelet-based algorithms
are compared to the currently used method, traditional convolution with
first-derivative Gaussian filters. The comparison analyses consists of
two parts: (a) the computational expense of the new method is compared
with the computational costs of the current method and (b) the outputs
of the wavelet-based methods are compared with those of the current method
to determine any practical differences in the resulting spectral fingerprints.
The results show that the wavelet-based algorithms can greatly reduce the
computational expense of generating spectral fingerprints, while practically
no differences exist in the resulting fingerprints. The analysis is conducted
on a database of hyperspectral signatures, namely, Hyperspectral Digital
Image Collection Experiment (HYDICE) signatures. The reduction in computational
expense is by a factor of about 30, and the average Euclidean distance
between resulting fingerprints is on the order of 0.02.
Brumbley, C.; I. C. Chein, (1998). Unsupervised linear unmixing Kalman
filtering approach to signature extraction and estimation for remotely
sensed imagery. IGARSS '98. Sensing and Managing the Environment. 1998
IEEE International Geoscience and Remote Sensing. Symposium Proceedings.
(Cat. No.98CH36174) Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.1590-2 vol.3.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Kalman filters; Remote sensing; Geophysical
measurement technique; Land surface; Terrain mapping; Image processing;
Unsupervised linear unmixing Kalman filtering; Signature extraction; Signature
matrix; Linear mixture model; Unsupervised learning ; Clustering algorithm
Original abstract: Linear Unmixing Kalman Filtering (LUKF) approach
was recently developed which incorporates the concept of linear unmixing
into Kalman filtering so as to achieve signature abundance estimation,
subpixel detection and classification for remotely sensed images. However,
LUKF assumes a complete knowledge of the signature matrix used in the linear
mixture model. In this paper, the LUKF is extended to an unsupervised LUKF
where no knowledge about the signature matrix is required a priori. The
unsupervised learning method proposed for the ULUKF is derived from a vector
quantization-based clustering algorithm. It employs a nearest-neighbor
rule to group potential signatures resident within an image scene into
a class of distinct clusters whose centers represent different types of
signatures. These clusters' centers are then used as if they were true
signatures in the signature matrix LUKF. In order to evaluate the effectiveness
of ULUKF, HYDICE images were used for assessment. The results produced
by ULUKF show that subpixel detection and classification can be performed.
Brumby, S. P.; N. R. Harvey; S. J. Perkins; R. B. Porter; J. J. Szymanski;
J. Theiler; J. J. Bloch, (2000). Genetic algorithm for combining new
and existing image processing tools for multispectral imagery. Algorithms
for Multispectral, Hyperspectral, and Ultraspectral Imagery VI Orlando,
FL, USA 24-26 April 2000
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.480-90.
Keywords: Feature extraction; Genetic algorithms; Image processing;
Remote sensing; Genetic algorithm; Multispectral imagery; Image processing
tools; Image processing algorithms; Chromosomal representation ; Geospatial
feature extraction
Original abstract: We describe the implementation and performance of
a genetic algorithm (GA) which evolves and combines image processing tools
for multispectral imagery (MSI) datasets. Existing algorithms for particular
features can also be "re-tuned" and combined with the newly evolved image
processing tools to rapidly produce customized feature extraction tools.
First results from our software system were presented previously. We now
report on work extending our system to look for a range of broad-area features
in MSI datasets. These features demand an integrated spatio-spectral approach,
which our system is designed to use. We describe our chromosomal representation
of candidate image processing algorithms, and discuss our set of image
operators. Our application has been geospatial feature extraction using
publicly available MSI and hyper-spectral imagery (HSI). We demonstrate
our system on NASA/Jet Propulsion Laboratory's Airborne Visible and Infrared
Imaging Spectrometer (AVIRIS) HSI which has been processed to simulate
MSI data from the Department of Energy's Multispectral Thermal Imager (MTI)
instrument. We exhibit some of our evolved algorithms, and discuss their
operation and performance.
Brunzell, H., (1997). Extraction of discriminant features from impulse
radar data for classification of buried objects. IGARSS'97. 1997 International
Geoscience and Remote Sensing Symposium. Remote Sensing - A Scientific
Vision for Sustainable Development (Cat. No.97CH36042)Singapore 3-8 Aug.
1997
New York, NY, USA IEEE, pp.1285-7 vol.3.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Military systems; Pattern classification;
Radar detection; Radar imaging; Radar signal processing; Terrestrial electricity;
Geophysical measurement technique; Ground penetrating radar; Military system;
Mine detection; Mine detector; Buried object detection; Explosive mine;
Radar remote sensing; Discriminant feature; Impulse radar; Buried landmine;
Plastic mine; Ceramic mine; Nonmetallic object ; Large bandwidth
Original abstract: This paper deals with the problem of detecting and
classifying buried objects. The application in mind when addressing this
problem is the detection of buried landmines. Modern landmines are to a
large extent made out of plastic and ceramic materials. This makes detection
with traditional sensors such as metal detectors and magnetometers almost
impossible. Another problem with these sensors is the high false alarm
rate induced by metallic debris from exploded bomb shells. A sensor type
that seems to have capability to overcome these problems is the impulse
radar. The impulse radar can detect nonmetallic objects buried in the ground.
The large bandwidth of the radar also gives additional information that
can be used for classification purposes. The classification abilities enable
discrimination between mines and stones and metallic debris, thus reducing
the false alarm rate. An important step towards good classification results
is to extract a set of features from measured data. The present paper elaborates
on properties that an admissible feature type must possess and shows that
the choice of features should be related both to the type of measurements
and the type of classifier used. A number of different feature types are
finally evaluated using measured data from an impulse radar system.
Bruzzone, L. (2000). An approach to feature selection and classification
of remote sensing images based on the Bayes rule for minimum cost.
IEEE Transactions on Geoscience and Remote Sensing, 38, (1,
pt.2): 429-38.
Keywords: Bayes methods; Feature extraction; Geophysical signal
processing; Geophysical techniques; Image classification; Remote sensing;
Terrain mapping; Geophysical measurement technique; Land surface; Optical
imaging; Image processing; Feature selection; Minimum cost; Bayes method;
Minimizing; Overall error; Land-cover ; Bayes rule for minimum cost
Original Abstract: Classification of remote-sensing images is usually
carried out by using approaches aimed at minimizing the overall error affecting
land-cover maps. However, in several remote-sensing problems, it could
be useful to perform classification by taking into account the different
consequences (and hence the different costs) associated with each kind
of error. This allows one to obtain land-cover maps in which the total
classification cost involved by errors is minimized, instead of the overall
classification error. To this end an approach to feature selection and
classification of remote-sensing images based on the Bayes rule for minimum
cost (BRMC) is proposed. In particular a feature-selection criterion function
is presented that permits one to select the features to be given as input
to a classifier by taking into account the different cost associated with
each confused pair of land-cover classes. Moreover, a classification technique
based on the BRMC and implemented by using a neural network is described.
The results of experiments carried out on a multisource data set concerning
the Island of Elba (Italy) point out the ability of the proposed minimum
cost approach to produce land-cover maps in which the consequences of each
kind of error are considered.
Bruzzone, L., (1998). Classification of remote-sensing images by
using the Bayes rule for minimum cost. IGARSS '98. Sensing and Managing
the Environment. 1998 IEEE International Geoscience and Remote Sensing.
Symposium Proceedings. (Cat. No.98CH36174)Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.1778-80 vol.4.
Keywords: Bayes methods; Geophysical signal processing; Geophysical
techniques; Image classification; Minimisation; Remote sensing; Geophysical
measurement technique; Land surface; Terrain mapping; Bayes rule; Minimum
cost; Minimization; Bayesian method; Feature selection; Feature extraction;
Land-cover map ; Minimum cost approach
Original abstract: An approach based on the Bayes rule for minimum
cost for feature selection and classification of remote-sensing images
is proposed. This approach allows one to achieve land-cover maps in which
the total cost involved by errors, instead of the total classification
error, is minimized. Experiments carried out on a multisource data set
of the Island of Elba (Italy) point out the effectiveness of the proposed
minimum cost approach.
Bruzzone, L.; D. F. Prieto; G. Silvano, (1999). Extraction and selection
of robust features for classification of multispectral remote-sensing images.
IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99
(Cat. No.99CH36293) Hamburg, Germany 28 June-2 July 1999
Piscataway, NJ, USA IEEE, pp.119-21 vol.1.
Keywords: Feature extraction; Geophysical signal processing; Image
classification; Remote sensing; Extraction; Selection; Robust features;
Classification; Multispectral remote-sensing images; Land-cover; Invariant
behavior ; Acquisition conditions
Original abstract: In this paper, we present an approach to the extraction
and selection of robust features for classification of multispectral remote-sensing
images. In particular, several robust features are proposed that, given
a specific land-cover class, aim to exhibit an invariant behavior versus
variations in the acquisition conditions of the images considered. In addition,
a technique is presented, which is able to adaptively select the most robust
features for a given problem.
Bruzzone, L.; F. Roli; S. B. Serpico (1995). An extension of the
Jeffreys-Matusita distance to multiclass cases for feature selection.
IEEE Transactions on Geoscience and Remote Sensing, 33, (6):
1318-21.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Optical information processing; Remote
sensing; Geophysical measurement technique; Optical imaging; Visible; Land
surface; Terrain mapping; Jeffreys-Matusita distance; Multiclass case;
Feature selection; Image processing; Bhattacharyya ; Multispectral imaging
Original Abstract: The problem of extending the Jeffreys-Matusita distance
to multiclass cases for feature-selection purposes is addressed and a solution
equivalent to the Bhattacharyya bound is presented. This extension is compared
with the widely used weighted average Jeffreys-Matusita distance both by
examining the respective formulae and by experimenting on an optical remote-sensing
data set.
Bruzzone, L.; S. B. Serpico, (1998). A new search algorithm for feature
selection in high-dimensional remote-sensing images. Image and Signal
Processing for Remote Sensing IV Barcelona, Spain 21-23 Sept. 1998
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.34-41.
Keywords: Feature extraction; Geophysical signal processing; Remote
sensing; Search problems; Search algorithm; Feature selection; High-dimensional
remote-sensing images; Sub-optimal search strategy; Hyperspectral sensors;
Constrained local extremes; Discrete binary space; Computational cost ;
AVIRIS sensor
Original abstract: A new sub-optimal search strategy suitable for feature
selection in high-dimensional remote-sensing images (e.g. images acquired
by hyperspectral sensors) is proposed. Such a strategy is based on a search
for constrained local extremes in a discrete binary space. In particular,
two different algorithms are presented that achieve a different trade-off
between effectiveness of selected features and computational cost. The
proposed algorithms are compared with the classical sequential forward
selection (SFS) and sequential forward floating selection (SFFS) sub-optimal
techniques: the first one is a simple but widely used technique; the second
one is considered to be very effective for high-dimensional problems. Hyperspectral
remote-sensing images acquired by the AVIRIS sensor are used for such comparisons.
Experimental results point out the effectiveness of the presented algorithms.
Buckley, M.; J. Yang (1997). Regularised shortest-path extraction.
Pattern Recognition Letters, 18, (7): 621-9.
Keywords: Computer vision; Dynamic programming; Edge detection;
Feature extraction; Object recognition; Remote sensing; Shortest-path extraction;
Active contours; Time-delayed dynamic programming; Discretization; Pixel
subdivision; Fracture detection; Borehole images; Road detection; Satellite
images ; Regularization
Original Abstract: Regularization of shortest-paths and active contours
has been considered and attempted by a number of workers. However, it was
not until the development of the "time-delayed dynamic programming" algorithm
of Amini et al. (1990) in the active contours context that a method was
found which was able to apply a simple and intuitive smoothness constraint
with an efficient computational scheme. We show that, when applied to the
shortest-path problem, this technique, gives rise to a simple and efficient
algorithm. However, we find that the method is not practically useful in
some situations because of discretization effects. A modification using
pixel subdivision is proposed, which to a large extent overcomes this problem.
The modified method is illustrated using two examples: fracture detection
in borehole images and road detection in satellite images.
Bullock, M. E.; S. R. Fairchild; T. J. Patterson; R. Haxton, (1996).
Automated map generation and update from high-resolution multispectral
imagery. Algorithms for Multispectral and Hyperspectral Imagery II
Orlando, FL, USA 9-11 April 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.91-103.
Keywords: Cartography; Feature extraction; Geographic information
systems; Image resolution; Remote sensing; Automated map generation; High-resolution
multispectral imagery; LOCATE TNG system; Lines of communication; Landsat
thematic mapper imagery; GIS applications; 30 m; 4 m ; 1 m
Original abstract: This paper describes the LOCATE TNG system, which
generates map products directly from multispectral imagery in an automated
fashion. The LOCATE TNG system uses spectral and spatial feature information
to extract various types of man-made lines of communication (LOCs) from
imagery and generate them in the form of digital vector maps. The generated
maps may be compared against reference digital maps to automatically find
new or changed LOCs. The original LOCATE (lines of communication apparent
from thematic mapper evidence) system was designed and developed to use
Landsat thematic mapper imagery having a resolution of 30 m. LOCATE TNG
(the next generation) has been redesigned to also have the capability to
use high-resolution multispectral imagery to be available from the next
generation of commercial satellites. These satellites will provide multispectral
and panchromatic imagery having resolutions down to 4 m and 1 m, respectively,
thus dramatically improving the information available for exploitation.
LOCATE TNG employs a hierarchical algorithmic approach to extracting layers
of LOCs (primary roads, secondary roads, etc.) that may be used for GIS
applications.
Burkhart, G. R.; Z. Bergen; R. Carande; W. Hensley; D. Bickel; J. R.
Fellerhoff, (1996). Elevation correction and building extraction from
interferometric SAR imagery. IGARSS '96. 1996 International Geoscience
and Remote Sensing Symposium. Remote Sensing for a Sustainable Future (Cat.
No.96CH35875) Lincoln, NE, USA 27-31 May 1996
New York, NY, USA IEEE, pp.659-61 vol.1.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image enhancement; Radar imaging; Radar target recognition;
Remote sensing by radar; Synthetic aperture radar; Geophysical measurement
technique; Land surface; Terrain mapping; Radar remote sensing; Urban area;
Town; City; Building; Elevation correction; Interferometric SAR imagery;
ifsar; High resolution; Man made structure; Industrial area; Tree; Forest;
Vegetation mapping ; Artifact removal
Original abstract: The development of high (2m) resolution interferometric
SAR (IFSAR) instrumentation makes extraction of man made and natural urban
structures feasible. In particular, the authors consider building extraction
from imagery of an urban/industrial area. IFSAR imagery are particularly
well suited for this task because these data include the measured elevation
as well as the coherence and intensity of the back scattered radiation.
Gradients in the IFSAR elevation correspond directly to elevation edges.
Coherence and intensity data can be combined to give specific information
about the scattering properties of the viewed surface. The disadvantage
of IFSAR imagery is that these data are typically of lower resolution and
contain greater noise than other data such as optical photography, also
the data contain specific artifacts that must be removed. Indeed, the motivation
for building and tree extraction behind this work is the need to remove
noise and artifacts from the IFSAR data. Techniques for removing artifacts
that are peculiar to IFSAR data are particularly discussed.
Butler, J. A., (1995). A comprehensive GIS-T enterprise database
design. GIS/LIS *95 Annual Conference and Exposition Proceedings of
Geographic Information Systems/Land Information Systems Nashville, TN,
USA 14-16 Nov. 1995
Bethesda, MD, USA American Soc. Photogrammetry & Remote Sensing
& American Congress on Surveying & Mapping, pp.137-46 vol.1.
Keywords: Electronic data interchange; Geographic information systems;
Government data processing; Public administration; Software standards;
Systems analysis; Transportation; Visual databases; GIS-T database design;
Enterprise database design; Business processes; State department of transportation;
Locationally referenced data; One-dimensional linear referencing method;
Information engineering principles; Multimodal agency; Spatial Data Transfer
Standard; sdts; Data exchange ; Geographic information system
Original abstract: While business processes differ between state departments
of transportation (DOTs), the data which they use are relatively consistent.
This paper proposes an enterprise GIS-T database design that can be generally
applied by all state DOTs and other agencies working with transportation
data which are locationally referenced. Of particular importance is the
design*s ability to work with data that utilize a one-dimensional linear
referencing method, and the correlation of that method to other locating
systems. The database design was developed by applying information engineering
principles to the needs of a multi-modal agency. This work builds on that
of the GIS-T/ISTEA Pooled-fund Study and NCHRP 20-27. It is based on the
federal Spatial Data Transfer Standard (SDTS), so it also serves to illustrate
how agencies could use that standard for data exchange. A set of implementation
choices is used to bring out details of the database design.
Caelli, T.; A. McCabe; G. Briscoe (2001). Shape tracking and production
using hidden Markov models. International Journal of Pattern Recognition
and Artificial Intelligence, 15, (1): 197-221.
Keywords: Cartography; Edge detection; Feature extraction; Hidden
Markov models; Image sequences; Maximum likelihood estimation; Pattern
classification; Probability; Remote sensing; Shape classification; Probability
distribution; Viterbi method; Search problem; Hamming distance; Symbol
sequences; Scene understanding ; Competitive unsupervised learning
Original Abstract: This paper deals with an application of hidden Markov
models (HMMs) to the generation of shape boundaries from image features.
In the proposed model, shape classes are defined by sequences of "shape
states" each of which has a probability distribution of expected image
feature types (feature "symbols"). The tracking procedure uses a generalization
of the well-known Viterbi method by replacing its search by a type of "beam-search"
so allowing the procedure, at any time, to consider less likely features
(symbols) as well the search for an optimal state sequences. We evaluated
the model performance on a variety of image and shape types, and developed
a new performance measure defined by an expected Hamming distance between
predicted and observed symbol sequences. Results point to the use of this
type of model for the depiction of shape boundaries when it is necessary
to have accurate boundary annotations as, for example, occurs in cartography.
Caetano, M.; J. Santos; A. Navarro, (1997). A multi-strategic approach
for land use mapping of urban areas by integrating satellite and ancillary
data. IGARSS'97. 1997 International Geoscience and Remote Sensing Symposium.
Remote Sensing - A Scientific Vision for Sustainable Development (Cat.
No.97CH36042)Singapore 3-8 Aug. 1997
New York, NY, USA IEEE, pp.240-2 vol.1.
Keywords: Geophysical signal processing; Geophysical techniques;
Image classification; Remote sensing; Geophysical measurement technique;
Land surface; Town; City; Urban area; Land use; Multi-strategic approach;
Mapping; Satellite remote sensing; Ancillary data; Method; Satellite imagery;
Road network; Census data; Contextual operators; Population; Habitation;
Land use class; Grande Lisboa; Lisbon ; Portugal
Original abstract: A methodology for land use mapping of urban areas
by using satellite imagery and ancillary data (road network and census
data) was developed. The integration of ancillary data was sequentially
done to allow a cost/benefit analysis. In the analysis of the satellite
imagery, special emphasis was put in the development of contextual operators
for (1) discriminating pixels that have different land uses but have similar
spectral characteristics, and (2) identifying land use classes that cannot
be identified at a pixel level. A road network map was integrated with
the satellite imagery for stratifying the study area into urban and rural
areas. This stratification allowed the application of different algorithms
to each stratum for an effective improvement of the land use mapping accuracy.
In addition, population and habitation census data were used for a refinement
of land use classes discrimination. The methodology was tested with a SPOT
image to generate a map with the CLUSTERS nomenclature for the Area da
Grande Lisboa, Portugal. In the final map, 22 land use classes were identified
with an overall accuracy of 88% (Kappa index). Most of these 22 classes
were identified with user's and producer's accuracy larger than 85%.
Cai, T.; R.-S. Wang (2001). Am algorithm for extracting road network
from multi-band remote sensing images. Journal of Software,
12, (6): 943-8.
Keywords: Feature extraction; Remote sensing; Sensor fusion; Transportation;
Road network; Remote sensing images; Multiband images ; Image recognition
Original Abstract: An algorithm for extracting road network by fusion
from multi-band remote sensing images is presented. First, straight lines
and parallel lines are extracted from multi-band images and fused in order
to overcome the uncertainty of the description of roads in the images.
Next, the roads that fit well with the constraints of the model of road
are recognized according to the local property of lines. Finally, the roads
that can not be fit well with the constraints of the model of road are
recognized by using the global connection constraints of road network.
The algorithm is applied on 3-band remote sensing images, and the effectiveness
is shown by the results.
Camiciottoli, R.; J. M. Corrifoni; A. D. Bimbo; E. Vicario; D. Lucarella
(1998). 3D navigation of geographic data sets. IEEE Multimedia,
5, (2): 29-41.
Keywords: Colour graphics; Data visualisation; Geographic information
systems; Transportation; User interfaces; Very large databases; 3D navigation;
Three dimensional navigation; Geographic data sets; Wide area transportation
networks; Large data sets; Continuous monitoring; Network links; Data types;
Visualization environment; Multiple presentation modes; 3D graphics; Color;
Windowing ; Three dimensional graphics
Original Abstract: Supervision and control of wide area transportation
networks requires continuous monitoring of large data sets. Two factors
complicate the process: data items are spread over a wide geographic area,
but are reciprocally influenced through network links, and data types attached
to network nodes belong to different categories. We describe a visualization
environment that tests the joint use of multiple presentation modes, such
as 3D graphics, color, and windowing, to address both factors.
Campbell, M. V.; K. R. Slocum; J. F. Moeller, (1996). Efficient extraction
of vegetation attributes from high-resolution multispectral imagery.
Proceedings of Eco-Informa '96. Global Networks for Environmental Information
Proceedings of Meeting on Global Networks for Environmental Information:
Bridging the Gap Between Knowledge and Application Lake Buena Vista, FL,
USA 4-7 Nov. 1996
Ann Arbor, MI, USA Environ. Res. Inst. Michigan, pp.387-92 vol.1.
Keywords: Data acquisition; Feature extraction; Forestry; Geographic
information systems; Image resolution; Interpolation; Remote sensing; Spectral
analysis; Statistical analysis; Vegetation attributes; High-resolution
multispectral imagery; Airborne video imagery; Forest; Georgia; Geostatistical
analyses; Field data collection; Accuracy assessment; Image interpretation;
gis; Stem spacing; Percent canopy closure; Species composition; Isopleth
maps; Surface interpolation; Sample size allocation ; Image spectral variability
Original abstract: A technique is described for extracting vegetation
attributes from high-spatial-resolution airborne multispectral video imagery
acquired over a forested study site in west-central Georgia. Geostatistical
analyses were used to allocate a systematic sample of the imagery and to
collect field data for accuracy assessment. Image interpretation and GIS
procedures produced estimates of stem spacing, percent canopy closure,
and species composition within sample plots. Isopleth maps for each attribute
were created using surface interpolation. Image interpretation results
exhibited moderate to poor accuracy when compared to the ground truth data.
The surface interpolation procedures created only moderately accurate isopleth
maps for each of the forest attributes. Potential sources of error include:
inadequate sample size allocation, image spectral variability associated
with date of acquisition, and inappropriate surface interpolation algorithms.
Possible solutions and future research areas are presented.
Cao, W.; Q. Qin (1998). A knowledge-based research for road extraction
from digital satellite images. Acta Scientiarum Naturalium Universitatis
Pekinensis, 34, (2-3): 254-63.
Keywords: Feature extraction; Geographic information systems; Image
recognition; Knowledge based systems; Object recognition; Remote sensing;
Road extraction; Digital satellite images; Satellite image recognition;
Geographical databases; Road properties; Image processing; Artificial intelligence;
Road detection; Shape index; Possible road points; Contextual information;
Road segment; Knowledge-based rules; SPOT data; Expert system ; Geographical
information system
Original Abstract: As basic geographical information, road extraction
from satellite images is important in the practice and theory of automatic
satellite image recognition, and its results can be used for updating geographical
databases. Using existing approaches to automatic road extraction from
satellite images for reference and considering the road properties in China,
this paper presents a new road extraction method that combines image processing
techniques with an artificial intelligence methodology. In this approach,
an operator is applied to an image to enhance the road information, then
road detection based on the shape index and other a priori knowledge is
performed to find possible road points. After using more contextual information
or global constraints to extend the road seeds to form road segments, the
existence of a road is inferred and the gap between the road segments is
connected by employing knowledge-based rules. Finally, experimental results
on SPOT data are shown.
Caorsi, S.; P. Gamba, (1998). Neural network approach for electromagnetic
inverse scattering solution. International Symposium on Electromagnetic
Theory. Proceedings of 1998 International Symposium on Electromagnetic
Theory (Commission B Triennial Open Symposium) Thessaloniki, Greece 25-28
May 1998, pp.524-6 vol.2.
Keywords: Backscatter; Electrical engineering computing; Electromagnetic
wave scattering; Inverse problems; Multilayer perceptrons; Permittivity;
Remote sensing; Neural network approach; Electromagnetic inverse scattering;
Three layer perceptron; Backscattered electromagnetic field; Geometric
characteristics; Electrical characteristics; Cylindrical object; Input
measurements; Dielectric permittivity; Cylinder radius ; Cylinder location
Original abstract: This paper is devoted to develop a neural approach
to the electromagnetic inverse scattering problem. In particular a three
layer perceptron is used to retrieve from the backscattered electromagnetic
field values the geometric and electrical characteristics of a cylindrical
object buried inside a given investigation domain. The number of input
measurements, as well as the network structure are investigated. We show
that the dielectric permittivity, location and radius of the cylinder can
be reliably computed from these inputs very quickly, allowing the use of
this approach in real-time remote sensing applications.
Carande, R. E.; M. Marra; D. Cronin; P. Nagy, (1998). Automated mapping
using airborne IFSAR data. IGARSS '98. Sensing and Managing the Environment.
1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings.
(Cat. No.98CH36174) Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.360-2 vol.1.
Keywords: Airborne radar; Feature extraction; Geophysical signal
processing; Geophysical techniques; Image classification; Radar imaging;
Remote sensing by radar; Synthetic aperture radar; Topography (Earth);
Geophysical measurement technique; Land surface; Terrain mapping; Radar
remote sensing; Automated mapping; Airborne IFSAR; sar; Two-antenna interferometric
SAR; Elevation; Topography; Topographic map; Geometric rectification; Automatic
map projection; Interferometric coherence; Land-use classification; Feature
detection; Algorithm ; Interferometric SAR
Original abstract: Two-antenna interferometric SAR instruments acquire
SAR data in such a manner that the signals may be combined and processed
to extract the elevation of each pixel. In addition to providing a high
resolution topographic map of the area, this allows for geometric rectification
and automatic map projection of the SAR image. The interferometric coherence
may be used to assist in land-use classification which can further exploited
for assisting in automated feature detection and extraction. This paper
describes and demonstrates algorithms suitable for automatic generation
of map products from Interferometric SAR data.
Carlotto, M. J., (1996). Detecting man-made features in SAR imagery.
IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.
Remote Sensing for a Sustainable Future (Cat. No.96CH35875) Lincoln, NE,
USA 27-31 May 1996
New York, NY, USA IEEE, pp.34-6 vol.1.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image matching; Image segmentation; Radar imaging; Radar signal
processing; Remote sensing by radar; Synthetic aperture radar; Weibull
distribution; Geophysical measurement technique; Radar remote sensing;
Land surface; Terrain mapping; Urban area; Buildings; Man-made feature;
SAR imagery; Local histogram; Weibull density; Median; Skewness ; Image
region analysis
Original abstract: A method for detecting man-made features in synthetic
aperture radar (SAR) imagery is described. The method is based on matching
the local histogram against a family of Weibull densities. The Weibull
density is defined by two parameters, the median and the skewness (Weibull
parameter). Regions containing man-made objects have Weibull parameter
values that are smaller than those containing natural features. In experiments
performed with aircraft SAR imagery, man-made features are effectively
discriminated from natural features using this method.
Carlotto, M. J., (1996). Nonlinear mean-square estimation with applications
in remote sensing. Algorithms for Multispectral and Hyperspectral Imagery
II Orlando, FL, USA 9-11 April 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.206-17.
Original abstract: An approach to image modeling based on nonlinear
mean-square estimation that does not assume a functional form for the model
is described. The relationship between input and output images is represented
in the form of a lookup table that can be efficiently computed from, and
applied to images. Three applications are presented to illustrate the utility
of the technique in remote sensing. The first illustrates how the method
can be used to estimate the values of physical parameters from imagery.
Specifically we estimate the topographic component (i.e., the variation
in brightness caused by the shape of the surface) from multispectral imagery.
The second application is a nonlinear change detection algorithm which
predicts one image as a nonlinear function of another. In cases where the
frequency of change is large (e.g., due to atmospheric and environmental
differences), the algorithm is shown to be superior in performance to linear
change detection. In the last application, a technique for removing wavelength-dependent
space-varying haze from multispectral imagery is presented. The technique
uses the IR bands, which are not affected significantly by haze, to predict
the visible bands. Results show a significant reduction in haze over the
area considered. Additional application areas are also discussed.
Carlotto, M. J., (1996). Using maps to automate the classification
of remotely-sensed imagery. Algorithms for Multispectral and Hyperspectral
Imagery II Orlando, FL, USA 9-11 April 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.40-50.
Original abstract: The accurate classification of remotely-sensed imagery
usually requires some form of ground truth data. Maps are potentially a
valuable source of ground truth but have several problems (e.g., they are
usually out-dated, features are generalized, and thematic categories in
the map often do not correspond to distinct clusters or segments in the
imagery). We describe several methods for using maps to automate the classification
of remotely-sensed data, specifically Landsat Thematic Mapper imagery.
In each, map data are coregistered to all or a part of the image to be
classified. A probability model relating spectral clusters derived from
the imagery to thematic categories contained in the map is then estimated.
This model is computed globally and adjusted locally based on context.
By computing the probability model over a large area (e.g., the full Landsat
scene) general relationships between spectral categories and clusters are
captured even though there are differences between the image and the map.
Then, by adjusting and applying the model locally, new features can be
extracted from the image that are not contained in the map and, in certain
cases, different classes can be assigned to the same cluster in different
parts of the image based on context. Experimental results are presented
for several Landsat scenes. Several of the methods produced results that
were more accurate than the map. We show that these methods are able to
enhance the spatial detail of features contained in the map, identify new
features not present in the map, and fill in areas in which map coverage
does not exist.
Carr, J. R.; K. Matanawi (1999). Correspondence analysis for principal
components transformation of multispectral and hyperspectral digital images.
Photogrammetric Engineering and Remote Sensing, V65, (N8):
909-914.
Keywords: Optics/Acoustics
Carrere, V.; J. E. Conel (1993). Recovery of Atmospheric Water Vapor
Total Column Abundance from Imaging Spectrometer Data around 940 Nm - Sensitivity
Analysis and Application to Airborne Visible Infrared Imaging Spectrometer
(Aviris) Data. Remote Sensing of Environment, V44, (N2-3):
179-204.
Keywords:
Cazzaniga, G.; A. Monti Guarnieri, (1996). Removing RF interferences
from P-band airplane SAR data. IGARSS '96. 1996 International Geoscience
and Remote Sensing Symposium. Remote Sensing for a Sustainable Future (Cat.
No.96CH35875) Lincoln, NE, USA 27-31 May 1996
New York, NY, USA IEEE, pp.1845-7 vol.3.
Keywords: Geophysical signal processing; Geophysical techniques;
Interference; Interference (signal); Interference filters; Notch filters;
Radar imaging; Radar interference; Remote sensing by radar; Synthetic aperture
radar; Geophysical measurement technique; Land surface; Terrain mapping;
UHF radar; Radar remote sensing; RF interference removal; P-band; Airplane
SAR; Airborne radar; music; Notch filtering; In-phase subtraction; Urban
area ; Adaptive signal processing
Original abstract: This paper approaches the problem of canceling the
disturbances due to RF interferences in P-band, airborne SAR missions.
Two techniques are introduced: one exploits MUSIC to estimate the interferences'
frequencies, and then performs notch filtering at that frequencies; whereas
the other adaptively estimate the interference contributions and cancel
them by means of in-phase subtraction. Both techniques have been successfully
tested on the data acquired by the DLR E-SAR sensor over urban areas.
Ceccarelli, M.; A. Farina; A. Petrosino, (1996). Fuzzy unsupervised
terrain classification based on a multiresolution approach. Proceedings
of the WILF '95. Italian Workshop on Fuzzy Logic 1995. New Trends in Fuzzy
Logic Naples, Italy 21-22 Sept. 1995
Singapore World Scientific, pp.151-9.
Keywords: Feature extraction; Fuzzy logic; Geophysics computing;
Image classification; Image resolution; Image texture; Radar computing;
Radar imaging; Radar theory; Real-time systems; Remote sensing; Synthetic
aperture radar; Unsupervised learning; Fuzzy unsupervised terrain classification;
Multiresolution approach; Real-time classification; Remote-sensed data;
Pattern recognition; Fuzzy clustering; Data grouping; Textural features;
Log-Gabor pyramidal analysis; Image analysis ; Spectral properties
Original abstract: Real-time classification of remote-sensed data is
a challenging application in the pattern recognition area, especially when
the spatial and temporal resolution increase. We focus on unsupervised
fuzzy clustering algorithms applied to synthetic aperture radar data grouping
and categorization. Fuzzy clustering methods are used as a classification
module of the textural features extracted by a log-Gabor pyramidal analysis
of the original image, whereas spectral properties of the areas to be recognised
are used for the choice of the feature extraction parameters.
Ceccarelli, M.; A. Petrosino, (1997). A generalized regularization
network for remote sensing data classification. Neural Nets WIRN VIETRI-96.
Proceedings of the 8th Italian Workshop on Neural Nets Salerno, Italy 23-25
May 1996
London, UK Springer-Verlag, pp.170-9.
Keywords: Feature extraction; Feedforward neural nets; Image classification;
Image resolution; Image texture; Parameter estimation; Real-time systems;
Remote sensing by radar; Statistical analysis; Synthetic aperture radar;
Generalized regularization network; Remote sensing data classification;
Real-time classification; Pattern recognition; Spatial resolution; Temporal
resolution; Neural network; Statistical methodologies; RBF networks; Synthetic
aperture radar data classification; Textural feature extraction; Log-Gabor
pyramidal analysis; Data-dependent approach; Spectral properties ; Radial
basis function neural network
Original abstract: Real-time classification of remotely sensed data
is a challenging application in the pattern recognition area, especially
when the spatial and temporal resolution increases. Several studies proved
that the neural network approach is an interesting alternative to conventional
statistical methodologies. We focus on RBF networks applied to synthetic
aperture radar data classification. RBF networks are used as a classification
module of the textural features extracted by a log-Gabor pyramidal analysis
of the original image. The problem of fast and reliable estimation of RBF
parameters is addressed and a data-dependent approach is proposed. In particular,
spectral properties of the areas to be recognised are used for the choice
of the feature extraction parameters, whereas the statistical properties
of the features are used for the suitable design of a generalized regularization
network.
Ceccarelli, M.; A. Petrosino; A. Farina, (1996). Recognition of terrain
classes in SAR images based on fuzzy methods and multichannel texture analysis.
Proceedings of the International Workshop on Soft Computing in Remote Sensing
Data Analysis Milan, Italy 4-5 Dec. 1995
Singapore World Scientific, pp.59-63.
Keywords: Cartography; Feature extraction; Fuzzy neural nets; Fuzzy
set theory; Image classification; Image texture; Remote sensing; Synthetic
aperture radar; Terrain class recognition; SAR images; Fuzzy methods; Multichannel
texture analysis; Real time classification; Remote sensed data; Pattern
recognition; Temporal resolution; Neural network approach; Unsupervised
fuzzy clustering algorithms; Synthetic Aperture Radar data grouping; Fuzzy
clustering networks; Classification module; Textural features; Log Gabor
pyramidal analysis; Network parameter estimation; Data dependent approach
; Spectral properties
Original abstract: Real time classification of remote sensed data is
a challenging application in pattern recognition, especially when the spatial
and temporal resolution increase. Several recent studies proved that the
neural network approach is an interesting alternative to conventional statistical
methodologies. We focus on unsupervised fuzzy clustering algorithms applied
to Synthetic Aperture Radar data grouping and classification. Fuzzy clustering
networks are used as a classification module of the textural features extracted
by a log Gabor pyramidal analysis of the original image. The problem of
fast and reliable estimation of the network parameters is addressed and
a data dependent approach is proposed. In particular, spectral properties
of the areas to be recognised are used for the choice of the feature extraction
parameters.
Cetin, H.; T. A. Warner; D. W. Levandowski (1993). Data Classification,
Visualization, and Enhancement Using N-Dimensional Probability Density
Functions (Npdf) - Aviris, Tims, Tm, and Geophysical Applications.
Photogrammetric Engineering and Remote Sensing, V59, (N12):
1755-1764.
Keywords: Optics/Acoustics
Chalasani, V.; P. A. Beling, (1998). Optimization based classifiers
for road extraction. SMC'98 Conference Proceedings. 1998 IEEE International
Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218) San Diego,
CA, USA 11-14 Oct. 1998
New York, NY, USA IEEE, pp.2938-43 vol.3.
Keywords: Decision trees; Feature extraction; Geography; Image classification;
Infrared imaging; Linear programming; Remote sensing; Optimization based
classifiers; Road extraction; Performance; Linear programming-based decision
tree; Gray scale images; AVIRIS images; Pixel classification; Discriminant
lines ; Nearest neighbour
Original abstract: We investigate the performance of a linear programming-based
decision tree in creating gray scale images for road extraction from AVIRIS
images. We apply our method for classification of pixels from a digital
image of an area near Williamsburg, Virginia, using the distance from discriminant
lines as a measure to create a gray scale image. Our method effectively
captures information from a large number of bands of the original image
and can be a useful input to other techniques which can use only a single
band.
Chang, C. I. (2000). An information-theoretic approach to spectral
variability, similarity, and discrimination for hyperspectral image analysis.
Ieee Transactions on Information Theory, V46, (N5): 1927-1932.
Keywords: spectral database; library hyperspectral image analysis,
spectral variability
Original Abstract: A hyperspectral image can be considered as an image
cube where the third dimension is the spectral domain represented by hundreds
of spectral wavelengths. As a result, a hyperspectral image pixel is actually
a column vector with dimension equal to the number of spectral bands and
contains valuable spectral information that can be used to account for
pixel variability, similarity and discrimination. We present a new hyperspectral
measure, the spectral information measure (SIM), to describe spectral variability
and two criteria, spectral information divergence and spectral discriminatory
probability for spectral similarity and discrimination, respectively. The
spectral information measure is an information-theoretic measure which
treats each pixel as a random variable using its spectral signature histogram
as the desired probability distribution. Spectral information divergence
(SID) compares the similarity between two pixels by measuring the probabilistic
discrepancy between two corresponding spectral signatures. The spectral
discriminatory probability calculates spectral probabilities of a spectral
database (library) relative to a pixel to be identified so as to achieve
material identification. In order to compare the discriminatory power of
one spectral measure relative to another, a criterion is also introduced
for performance evaluation, which is based on the power of discriminating
one pixel from another relative to a reference pixel. The experimental
results demonstrate that the new hyperspectral measure can characterize
spectral variability more effectively than the commonly used spectral angle
mapper (SAM).
--------------------------------------------------------------------------------
Chang, C. I.; H. Ren (2000). An experiment-based quantitative and
comparative analysis of target detection and image classification algorithms
for hyperspectral imagery. Ieee Transactions on Geoscience and Remote
Sensing, V38, (N2 PT2): 1044-1063.
Keywords: linear spectral unmixing hyperspectral image analysis
Original Abstract: Over the past years, many algorithms have been developed
for multispectral and hyperspectral image classification. A general approach
to mixed pixel classification is linear spectral unmixing, which uses a
linear mixture model to estimate the abundance fractions of signatures
within a mixed pixel. As a result, the images generated for classification
are usually gray scale images, where the gray level value of a pixel represents
a combined amount of the abundance of spectral signatures residing in this
pixel. Due to a lack of standardized data, these mixed pixel algorithms
have not been rigorously compared using a unified framework. The authors
present a comparative study of some popular classification algorithms through
a standardized HYDICE data set with a custom-designed detection and classification
criterion. The algorithms to be considered for this study are those developed
for spectral unmixing, the orthogonal subspace projection (OSP), maximum
likelihood, minimum distance, and Fisher's linear discriminant analysis
(LDA). In order to compare mixed pixel classification algorithms against
pure pixel classification algorithms, the mixed pixels are converted to
pure ones by a designed mixed-to-pure pixel converter. The standardized
HYDICE data are then used to evaluate the performance of various pure and
mixed pixel classification algorithms. Since all targets in the HYDICE
image scenes can be spatially located to pixel level, the experimental
results can be presented by tallies of the number of targets detected and
classified for quantitative analysis.
Chanussot, J.; G. Mauris; P. Lambert (1999). Fuzzy fusion techniques
for linear features detection in multitemporal SAR images. IEEE
Transactions on Geoscience and Remote Sensing, 37, (3, pt.1):
1292-305.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image sequences; Radar imaging; Remote sensing by radar; Sensor
fusion; Spaceborne radar; Synthetic aperture radar; Terrain mapping; Geophysical
measurement technique; Land surface; Land use; Road network; Fuzzy fusion;
Data fusion; Image fusion; Radar remote sensing; Linear feature detection;
Multitemporal SAR image; Automatic detection; Linear feature ; sar
Original Abstract: This paper is concerned with the automatic detection
of linear features in SAR satellite data, with application to road network
extraction. After a directional prefiltering step, a morphological line
detector is presented. To improve the detection performances, the results
obtained on multitemporal data are fused. Different fusion strategies involving
different fusion operators are then presented. Since extensions of classical
set union and intersection do not lead to satisfactory results (the corresponding
operators are either too indulgent or too severe), the first strategy consists
of fusing the data using a compromise operator. The second strategy consists
of fusing the results computed with two operators that have opposite properties,
in order to obtain a final intermediate result. Thanks to the wide range
of properties they provide, fuzzy operators are used to test and compare
these two fusion strategies on real ERS-1 multitemporal data.
Chanussot, J.; G. Mauris; P. Lambert, (1999). Improving road detection
on SAR images using fuzzy fusion methods. IMTC/99. Proceedings of the
16th IEEE Instrumentation and Measurement Technology Conference (Cat. No.99CH36309)
Measurements for the New Millennium Venice, Italy 24-26 May 1999
Piscataway, NJ, USA IEEE, pp.1807-12 vol.3.
Keywords: Cartography; Edge detection; Feature extraction; Fuzzy
set theory; Radar imaging; Remote sensing by radar; Sensor fusion; Terrain
mapping; SAR images; Road detection; Fuzzy fusion methods; Automatic detection;
Linear features; Multi-temporal images; Data fusion; Compromise operator;
Operators with opposite properties; Global intermediate result; Fuzzy operators;
Real ERS-1 data; Mean operator; Order weighted averaging operator; Feature
detection ; Line detection
Original abstract: This paper focuses on the use of fuzzy fusion techniques
to improve the automatic detection of linear features on multi-temporal
SAR images. Different fusion strategies involving different fusion operators
are presented. Since T-norms and T-conorms do not lead to satisfactory
results (these operators are respectively too severe and too indulgent),
the first strategy consists in fusing the data using a compromise operator.
The second strategy consists in fusing the results computed with two operators
with opposite properties, in order to obtain a global intermediate result.
Thanks to the wide range of behaviours they provide, fuzzy operators are
used to test and compare these two fusion strategies on real ERS-1 data.
Chein, I. C.; R. Hsuan (2000). An experiment-based quantitative and
comparative analysis of target detection and image classification algorithms
for hyperspectral imagery. IEEE Transactions on Geoscience and Remote
Sensing, 38, (2, pt.2): 1044-63.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Multidimensional signal processing; Remote
sensing; Terrain mapping; Geophysical measurement technique; Land surface;
Quantitative method; Target detection; Algorithm; Hyperspectral imagery;
Multispectral remote sensing; Linear spectral unmixing; Linear mixture
model; Gray scale image; Orthogonal subspace projection; Maximum likelihood;
Minimum distance ; Fisher's linear discriminant analysis
Original Abstract: Over the past years, many algorithms have been developed
for multispectral and hyperspectral image classification. A general approach
to mixed pixel classification is linear spectral unmixing, which uses a
linear mixture model to estimate the abundance fractions of signatures
within a mixed pixel. As a result, the images generated for classification
are usually gray scale images, where the gray level value of a pixel represents
a combined amount of the abundance of spectral signatures residing in this
pixel. Due to a lack of standardized data, these mixed pixel algorithms
have not been rigorously compared using a unified framework. The authors
present a comparative study of some popular classification algorithms through
a standardized HYDICE data set with a custom-designed detection and classification
criterion. The algorithms to be considered for this study are those developed
for spectral unmixing, the orthogonal subspace projection (OSP), maximum
likelihood, minimum distance, and Fisher's linear discriminant analysis
(LDA). In order to compare mixed pixel classification algorithms against
pure pixel classification algorithms, the mixed pixels are converted to
pure ones by a designed mixed-to-pure pixel converter. The standardized
HYDICE data are then used to evaluate the performance of various pure and
mixed pixel classification algorithms. Since all targets in the HYDICE
image scenes can be spatially located to pixel level, the experimental
results can be presented by tallies of the number of targets detected and
classified for quantitative analysis.
Chen, C. C., (1998). A GIS approach to dynamic network routing.
Proceedings. Conference XXI. Enterprise-Wide Geospatial Solutions: Realizing
the Benefits Proceedings of AM/FM International Annual Conference San Jose,
CA, USA 26-29 April 1998
Aurora, CO, USA AM & FM Int, pp.441-9.
Keywords: Automated highways; Data handling; Geographic information
systems; Network routing; Parallel algorithms; Road traffic; Spatial data
structures; Transportation; gis; Dynamic network routing; Intelligent transportation
systems; Dynamic traffic assignment algorithm; Data integration; Real-time
traffic information; Embedding; Decomposable data structures; Parallel
processing algorithms; Routing systems; Time constraints; Location referencing
system ; Spatial data
Original abstract: Within the domain of intelligent transportation
systems (ITS) research, models devoted to solving dynamic traffic problems
are proposed by means of integrating historical data with real-time traffic
information. Due to the complexity of these models and the strength of
the spatial analyzing capability of GIS, a trend towards embedding these
algorithms into GIS is emerging. However, bottlenecks exist because: (1)
current GIS mainly deal with data that are not sensitive to temporal changes;
(2) there is a need for decomposable data structures in GIS to facilitate
parallel processing algorithms to meet the time constraints of dynamic
(or so called real-time) routing systems; and (3) there is a need for a
consistent location referencing system to integrate spatial data from various
sources. This paper discusses these difficulties in detail and proposes
a conceptual model by integrating GIS with a dynamic traffic assignment
(or dynamic route choice) algorithm (DTA) to resolve this problem.
Chen, C. H., (1997). Trends on information processing for remote
sensing. IGARSS'97. 1997 International Geoscience and Remote Sensing
Symposium. Remote Sensing - A Scientific Vision for Sustainable Development
(Cat. No.97CH36042) Singapore 3-8 Aug. 1997
New York, NY, USA IEEE, pp.1190-2 vol.3.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image processing; Image segmentation; Neural nets; Pattern
recognition; Remote sensing; Wavelet transforms; Geophysical measurement
technique; Land surface; Terrain mapping; Information processing; Compression;
Segmentation; Neural network ; Wavelet transform
Original abstract: There has been greatly increased activity in the
last twelve years on the use of information processing techniques on remote
sensing problems including signal/image processing, compression, segmentation,
feature extraction, pattern recognition, neural networks, etc. The past
progress is reviewed from which a trend is developed. The trend shows a
further emphasis on using neural networks and wavelet transforms for remote
sensing.
Chen, C. H.; X. Zhang, (1999). Independent component analysis for
remote sensing study. Image and Signal Processing for Remote Sensing
V Florence, Italy 22-24 Sept. 1999
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.150-8.
Keywords: Airborne radar; Feature extraction; Geophysical signal
processing; Image classification; Neural nets; Principal component analysis;
Radar computing; Radar imaging; Remote sensing by radar; Synthetic aperture
radar; Independent component analysis; Remote sensing study; ica; Source
signal separation; Neural network architecture; SAR imagery; Pixel classification;
Contrast ratio; Speckle effect; ATM images; De-mixing operations ; Airborne
thematic mapper images
Original abstract: There has been much interest in the independent
component analysis (ICA) methods for source signal separation. ICA algorithms
can be represented by a neural network architecture to decompose a signal
or image into components. The potential use of ICA in remote sensing study
is examined. For SAR imagery in particular, the use of ICA to enhance the
images and to improve the pixel classification is considered. It is shown
that ICA processed images generally have lower contrast ratio (standard
deviation to mean of an image) which implies a reduced speckle effect.
The features extracted by using ICA also are quite effective for pixel
classification. There are five pattern classes considered. By using the
9 original SAR images plus all 6 ATM (airborne thematic mapper) images,
the best overall percentage correct is 86.6% which is the same as using
3 ICA and 6 ATM image data. Also ICA is shown to be better than PCA in
classification with the same data set. Although the results presented are
preliminary, ICA through its de-mixing operations is potentially a useful
approach in remote sensing study.
Chen, K. S.; Y. C. Tzeng; C. T. Chen; J. S. Lee, (1997). Filtering
effects on polarimetric SAR image classification. IGARSS'97. 1997 International
Geoscience and Remote Sensing Symposium. Remote Sensing - A Scientific
Vision for Sustainable Development (Cat. No.97CH36042) Singapore 3-8 Aug.
1997
New York, NY, USA IEEE, pp.1199-201 vol.3.
Keywords: Feature extraction; Forestry; Fuzzy neural nets; Geophysical
signal processing; Geophysical techniques; Geophysics computing; Image
classification; Radar imaging; Radar polarimetry; Remote sensing by radar;
Synthetic aperture radar; Geophysical measurement technique; Land surface;
Terrain mapping; Remote sensing; Filtering effects; Polarimetric SAR; Radar
remote sensing; Polarimetric filter; Supervised fuzzy dynamic learning;
Neural network; Fuzzy neural net; Kalman filter; Training; P-band; Tree
age classification; Vegetation mapping; Flevoland; Netherlands ; Land cover
boundary
Original abstract: Feature extraction from SAR images is usually impeded
by the presence of speckle noise. This becomes more serious in the case
of polarimetric SAR system. A polarimetric filter recently proposed by
Lee et al. [1997] emphasizes not introducing additional cross-talk and
statistical correlation between channels, preserving polarimetric information
and not degrading the image quality. This paper exams its effects on the
image classification by a supervised fuzzy dynamic learning neural network
trained by a Kalman filter technique. Based on the available ground truth,
the classification performance were evaluated using the original and filtered
SAR images. Two independent test sites are selected for this purpose. The
first case is a P-band JPL polarimetric SAR data over Les Landes for tree
age classification. A total of 12 classes between 5 to 44 years of age
were to be classified, along with a bare soil type. The second test site
is over Flevoland of the Netherlands. This agricultural site consists of
11 landcover types. Again, the polarimetric SAR data were acquired with
JPL P, L, C bands airsar system. For the first case, it was found that
the overall classification accuracy was able to improve from 69% to about
86% with kappa coefficient up from 0.46 to 0.76. Substantial improvement
was also confirmed for the second case. In particular, when classification
was performed using only single frequency. This shows that the polarimetric
information are well preserved. By visual inspection from classified map,
the land cover boundaries were also delineated more clearly. As for fuzzy
neural network performance, among the tested cases, the fuzzy index equal
to 2 gets the best results.
Chen, K. S.; S. K. Yen; D. W. Tsay (1997). Neural classification
of SPOT imagery through integration of intensity and fractal information.
International Journal of Remote Sensing, 18, (4): 763-83.
Keywords: Filtering theory; Fractals; Image classification; Kalman
filters; Learning (artificial intelligence); Multilayer perceptrons; Optimisation;
Remote sensing; Wavelet transforms; Neural classification; Information
integration; Intensity information; Fractal information; High-dimensional
information; Remotely sensed image classification; Land cover classification;
SPOT-HRV imagery; Multispectral intensity; Texture information; Fractal
dimension extraction; Wavelet transform; Image texture; Modified multilayer
perceptron; Kalman filtering; Fast convergence; Built-in optimization function;
Correlation analysis; Discrimination capability; Heterogeneous area; Urban
regions ; Open water
Original Abstract: It is well known that higher dimensional information
essentially leads to better accuracy in remotely sensed image classification.
This paper is aimed at land cover classification from SPOT-HRV imagery
by the integration of multispectral intensity and texture information.
In particular, fractal dimensions are extracted using a wavelet transform
as image texture. A neural network approach to classification is adopted
in this paper. The underlying network is a modified multilayer perceptron
trained by a Kalman filtering technique. The main advantages of this network
are (1) its nonbackpropagation fashion of learning which leads to a fast
convergence, (2) a built-in optimization function, and (3) global scale.
Saving computer storage space and a fast learning capability are in particular
suitable features for remote sensing applications. Correlation analysis
was subsequently performed on both the intensity and fractal images. It
was found that fractal information significantly improves the discrimination
capability of heterogeneous area such as in urban regions, while it slightly
degrades accuracy for homogeneous areas, such as open water. The overall
classification performance is superior to results obtained using reflectance
only. Improvements over heterogeneous areas are demonstrated.
Chen, S. P.; S. Zeng; C. G. Xie (2000). Remote sensing and GIS for
urban growth analysis in China. Photogrammetric Engineering and
Remote Sensing, V66, (N5): 593-598.
Keywords: Optics/Acoustics
Original Abstract: The progress of urban remote sensing and GIS in
China since the early 1980s is reviewed. The first section introduces the
early applications of remote sensing to environmental monitoring and resources
investigation, and outlines its achievements. The second section focuses
on further analysis of urban expansion from the point of view of spatial
distribution patterns and temporal change, taking Beijing, Shanghai, and
Dongguan as examples. Urban GIS is discussed in the third section. The
regional differences of UGIS development in China are detailed from south
to north. As remote sensing and GIS technologies develop, they will be
combined for use in urban planing and management.
Chen, Z.; T. J. Feng; Z. Houkes, (1999). Texture segmentation based
on wavelet and Kohonen network for remotely sensed images. IEEE SMC'99
Conference Proceedings. 1999 IEEE International Conference on Systems,
Man, and Cybernetics (Cat. No.99CH37028) Tokyo, Japan 12-15 Oct. 1999
Piscataway, NJ, USA IEEE, pp.816-21 vol.6.
Keywords: Feature extraction; Image segmentation; Image texture;
Remote sensing; Self-organising feature maps; Wavelet transforms; Kohonen
self-organizing map; 2D wavelet transform; Neural network ; Fuzzy clustering
Original abstract: In this paper, an approach based on wavelet decomposition
and Kohonen's self-organizing map is developed for image segmentation.
After performing the 2D wavelet transform of image, some features are extracted
for texture segmentation, and the Kohonen neural network is used to accomplish
feature clustering. The experimental results demonstrated the satisfactory
effect of the proposed approach both for simulated textured image and multi-spectral
remotely sensed images.
Chen, Z. K.; C. D. Elvidge; D. P. Groeneveld (1998). Monitoring seasonal
dynamics of arid land vegetation using AVIRIS data. Remote Sensing
of Environment, V65, (N3): 255-266.
Keywords:
Original Abstract: Seasonal changes in the density of photosynthetically
active vegetation have been observed in derivative-based green vegetation
index (DGVI) values derived from AVIRIS reflectance spectra of arid land
shrub and saltgrass communities adjacent to Mono Lake, California. The
study was conducted using AVIRIS datasets acquired in late August and early
October of 1992. 2DZ_DGVI (second-order DGVI-derived in reference to zero
baseline) showed a strong linear relationship (r2>0.93 for August and October)
with green leaf area index (LAI) values of ten bitterbrush (Purshia tridentata)
sample stands. After accounting for background errors introduced to the
calculation of 1DL_DGVI (first-order DGVI-derived in reference to local
rock-soil baseline), a modified 1DL_DGVI (1DL_MDGVI) exhibited a high linear
correlation (r2> 0.92 for both seasons) with green LAI values of the bitterbrush
stands. 2DZ_DGVI was applied to the two AVIRIS scenes acquired for the
two periods to quantify and compare green vegetation cover. Areas covered
by saltgrass (Distichlis spicata var. stricta) showed the largest change
in 2DZ_DGVI from August to October. Shrubs, including bitterbrush and big
sagebrush (Artemisia tridentata), changed less during the same period.
The lowest seasonal change in 2DZ_DGVI occurred in barren areas and locations
covered by Jeffrey pine (Pinus jeffreyi). The DGVI concept has potential
for monitoring ecosystems in arid and semiarid lands where the influence
of exposed rock¯soil backgrounds reduces the effectiveness of broadband
red-vs.-NIR vegetation indices.
Cheng, P.; T. Toutin, (1997). Urban planning using data fusion of
satellite and aerial photo images. IGARSS'97. 1997 International Geoscience
and Remote Sensing Symposium. Remote Sensing - A Scientific Vision for
Sustainable Development (Cat. No.97CH36042) Singapore 3-8 Aug. 1997
New York, NY, USA IEEE, pp.839-41 vol.2.
Keywords: Geophysical signal processing; Geophysical techniques;
Image processing; Remote sensing; Remote sensing by radar; Sensor fusion;
Town and country planning; Geophysical measurement technique; Town planning;
Urban area; Optical imaging; Radar; Terrain mapping; Land use; Land surface;
Data fusion; Aerial photo image; Satellite remote sensing; Multisource
data; Radiometric processing; Geometric processing; spot; radarsat; irs;
Photogrammetric method; Resection ; Urban planning
Original abstract: Urban planning using data fusion of different satellite
and aerial photo images can be very useful. However, multisource data fusion
requires geometric and radiometric processing, adapted to the nature and
characteristics of the data. In this way the best information available
from each image is preserved in the composite image. With the increased
resolution of satellite and aerial photo images (5 m and less), the off-nadir
viewing angle of the satellite sensor (greater than 20 degrees), and the
multi-source data available (such as SPOT, RADARSAT, and IRS), a general
and accurate photogrammetric method which can deal with different satellite
images and an accurate photogrammetric method for aerial photos are needed.
For satellite images, a rigorous method developed at the Canada Centre
for Remote Sensing (CCRS), Natural Resources Canada, which takes into account
the nature of the data can be used. For aerial photos, the method of space
resection by collinearity can be used. This paper presents data fusion
results using SPOT, RADARSAT, IRS satellite images and an aerial photo.
The results are sharp and precise, which enables a better and easier interpretation
for urban planning.
Chettri, S.; N. Netanyahu, (1997). Spectral unmixing of remotely
sensed imagery using maximum entropy. 25th AIPR Workshop. Emerging
Applications of Computer Vision Washington, DC, USA 16-18 Oct. 1996
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.55-62.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Information theory; Maximum entropy methods;
Remote sensing; Remotely sensed imagery; Spectral unmixing; Maximum entropy
formulation; Content extraction; Single picture element; Sub-pixel content
extraction; Automatically guaranteed positive fractions; Ground cover class
fractions; Combinatorial properties; Information theoretic entropy; Land
surface; Terrain mapping ; Geophysical measurement technique
Original abstract: The paper addresses the importance of a maximum
entropy formulation for the extraction of content from a single picture
element in a remotely sensed image. Most conventional classifiers assume
a winner take all procedure in assigning classes to a pixel whereas in
general it is the case that there exists more than one class within the
picture element. There have been attempts to perform spectral unmixing
using variants of least squares techniques, but these suffer from conceptual
and numerical problems which include the possibility that negative fractions
of ground cover classes may be returned by the procedure. In contrast,
a maximum entropy (MAXENT) based approach for sub-pixel content extraction
possesses the useful information theoretic property of not assuming more
information than is given, while automatically guaranteeing positive fractions.
The authors apply MAXENT to obtain the fractions of ground cover classes
present in a pixel and show its clear numerical superiority over conventional
methods. The optimality of this method stems from the combinatorial properties
of the information theoretic entropy.
Chiang, S. S.; C. I. Chang; I. W. Ginsberg (2001). Unsupervised target
detection in hyperspectral images using projection pursuit. IEEE
Transactions on Geoscience and Remote Sensing, 39, (7): 1380-91.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image classification; Image processing; Multidimensional signal
processing; Remote sensing; Terrain mapping; Geophysical measurement technique;
Land surface; Multispectral remote sensing; Hyperspectral remote sensing;
Unsupervised target detection; Hyperspectral image; Projection pursuit;
Man-made target; Skewness ; Trapping local optima
Original Abstract: The authors present a projection pursuit (PP) approach
to target detection. Unlike most of developed target detection algorithms
that require statistical models such as linear mixture, the proposed PP
is to project a high dimensional data set into a low dimensional data space
while retaining desired information of interest. It utilizes a projection
index to explore projections of interestingness. For target detection applications
in hyperspectral imagery, an interesting structure of an image scene is
the one caused by man-made targets in a large unknown background. Such
targets can be viewed as anomalies in an image scene due to the fact that
their size is relatively small compared to their background surroundings.
As a result, detecting small targets in an unknown image scene is reduced
to finding the outliers of background distributions. It is known that "skewness,"
is defined by normalized third moment of the sample distribution, measures
the asymmetry of the distribution and "kurtosis" is defined by normalized
fourth moment of the sample distribution measures the flatness of the distribution.
They both are susceptible to outliers. So, using skewness and kurtosis
as a base to design a projection index may be effective for target detection.
In order to find an optimal projection index, an evolutionary algorithm
is also developed to avoid trapping local optima. The hyperspectral image
experiments show that the proposed PP method provides an effective means
for target detection.
Chibani, Y.; A. Houacine, (2000). On the use of the redundant wavelet
transform for multisensor image fusion. ICECS 2000. 7th IEEE International
Conference on Electronics, Circuits and Systems (Cat. No.00EX445)Jounieh,
Lebanon 17-20 Dec. 2000
Piscataway, NJ, USA IEEE, pp.442-5 vol.1.
Keywords: Feature extraction; Image reconstruction; Redundancy;
Sensor fusion; Wavelength division multiplexing; Redundant wavelet transform;
Multisensor image fusion; Feature duplication; Scales ; Remote sensing
images
Original abstract: This paper describes a multisensor image fusion
scheme based on the use of the redundant wavelet transform. This transform
duplicates features through scales when they are significantly dominant.
In order to ensure that a fused image contains all significant features
coming from multisensor images, we exploit the property of redundancy to
develop a fusion rule applied in the wavelet domain. Simulated and remote
sensing images are used to evaluate the fusion results and compare the
performances for various rules.
Chih-Cheng, H.; A. Fahsi; W. Tadesse; T. Coleman, (1997). A comparative
study of remotely sensed data classification using principal components
analysis and divergence. 1997 IEEE International Conference on Systems,
Man, and Cybernetics. Computational Cybernetics and Simulation (Cat. No.97CH36088-5)
Orlando, FL, USA 12-15 Oct. 1997
New York, NY, USA IEEE, pp.2444-9 vol.3.
Keywords: Feature extraction; Image classification; Remote sensing;
Statistical analysis; Data bands; Principal components analysis; Divergence;
Multispectral image classification; Statistical separability; Feature selection;
Aerial photographs ; Landsat Thematic Mapper data
Original abstract: This paper investigates the principal components
analysis (PCA) and divergence for transforming and selecting data bands
for multispectral image classification. As the principal components are
independent of one another, a color combination of the first three components
can be useful in providing maximum visual separability of image features.
Therefore, principal components analysis is used to generate a new set
of data. Divergence, a measurement of statistical separability, is employed
as a method of feature selection to choose the optimal m-band subset from
the n-band data for use in the automated classification process. Classification
accuracy assessment is carried out using large scale aerial photographs.
Classification results on the Landsat Thematic Mapper (TM) data show that
PCA is a more effective approach than divergence.
Chitroub, S.; B. Sansal, (1997). Feature reduction in terms of higher
statistical separability for enhancement of multispectral image classification.
Proceedings of the Fourth IEEE International Conference on Electronics,
Circuits and Systems (ICECS'97) Cairo, Egypt 15-18 Dec. 1997, pp.1209-13
vol.3.
Keywords: Feature extraction; Image classification; Image representation;
Remote sensing; Feature reduction; Statistical separability; Multispectral
image classification ; Pattern recognition
Original abstract: In remote sensing pattern recognition application,
establishing an optimal multispectral image representation is important
for a better discrimination of scene targets. In fact, the spectral bands
that constitute the original multispectral image are not all interesting
for the problem of classification. A feature reduction consists to transform
the original pixel vector into a new of coordinates in which the new features
to be retained and those can be removed are made more evident. In this
paper, a method of feature reduction for the purpose of improving the accurate
and the speed of classification of remote sensing imagery is presented.
The method transforms the original space to the new subspace in which class
separation is optimized. Only the few first new images that exhibit a higher
statistical separability between classes are used for classification. The
method is tested and evaluated on TM images. The results are given in interesting
images forms.
Choi, K.; W. Jang (2000). Development of a transit network from a
street map database with spatial analysis and dynamic segmentation.
Transportation Research Part C-Emerging Technologies, V8,
(N1-6): 129-146.
Keywords: Transit network development; GIS; Digital map; Spatial
analysis; Dynamic segmentation
Original Abstract: This paper presents an integrated transit-oriented
travel demand modeling procedure within the framework of geographic information
systems (GIS). Focusing on transit network development, this paper presents
both the procedure and algorithm for automatically generating both link
and line data for transit demand modeling from the conventional street
network data using spatial analysis and dynamic segmentation. For this
purpose, transit stop digitizing, topology and route system building, and
the conversion of route and stop data into link and line data sets are
performed. Using spatial analysis, such as the functionality to search
arcs nearest from a given node, the nearest stops are identified along
the associated links of the transit line, while the topological relation
between links and line data sets can also be computed using dynamic segmentation.
The advantage of this approach is that street map databases represented
by a centerline can be directly used along with the existing legacy urban
transportation planning systems (UTPS) type travel modeling packages and
existing GIS without incurring the additional cost of purchasing a full-blown
transportation GIS package. A small test network is adopted to demonstrate
the process and the results. The authors anticipate that the procedure
set forth in this paper will be useful to many cities and regional transit
agencies in their transit demand modeling process within the integrated
GIS-based computing environment.
Choi, K.; T. J. Kim (1996). A hybrid travel demand model with GIS
and expert systems. Computers, Environment and Urban Systems,
20, (4-5): 247-59.
Keywords: Expert systems; fortran; Geographic information systems;
Human factors; Interactive systems; Planning; Transportation; User interfaces;
Hybrid travel demand model; gis; Transportation planning; Geographic information
system; Interactive system; TranDASS; User-unfriendliness; Labor-intensiveness;
Topology conversion algorithm ; User-friendly interface
Original Abstract: The purpose of this paper is to combine a traditional
transportation planning model with a geographic information system (GIS)
and an expert system (ES) in order to demonstrate the feasibility of integrating
a transportation planning model with GIS and ES technology. Hence an interactive
desktop transportation planning system called TranDASS was developed. By
combining the three systems, it is hoped that the inherent problems of
transportation planning models, such as user-unfriendliness, labor-intensiveness,
and theoretical limitations, can be alleviated. Yet combining GIS with
a traditional transportation planning model always means that one has to
overcome the topological incompatibility between the two. Therefore, a
FORTRAN-based topology conversion algorithm is developed that establishes
a communication channel between them. The expert system facilitates the
generation of input to the transportation planning model by providing a
user-friendly interface. Using an expert system, even in this limited sense,
sheds light on how to resolve judgmental issues in the transportation planning
process.
Chung-Sheng, L.; V. Castelli, (1997). Deriving texture feature set
for content-based retrieval of satellite image database. International
Conference on Image Processing (Cat. No.97CB36144) Santa Barbara, CA, USA
26-29 Oct. 1997
Los Alamitos, CA, USA IEEE Comput. Soc
Proceedings., pp.576-9 vol.1.
Keywords: Feature extraction; Image texture; Remote sensing; Visual
databases; Deriving texture feature set; Content-based retrieval; Satellite
image database; Performance; Transformed-based texture features; Spatial-based
texture features; Benchmark; Brodatz set; Normalized Euclidean distance;
Gabor filter; Transformed-based feature sets ; Quadrature mirror filter
Original abstract: In this paper, the performance of similarity retrieval
from satellite image databases by using different sets of spatial and transformed-based
texture features is evaluated and compared. A benchmark consisting of 37
satellite image clips from various satellite instruments is devised for
the experiments. We show that although the proposed feature set perform
only slightly better with the Brodatz set, its performance is far superior
for the satellite images. The result indicates that more than 25% of the
benchmark patterns can be retrieved with more than 80% accuracy by using
normalized Euclidean distance. In contrast, less than 10% of the patterns
are retrieved with more than 80% accuracy by using transformed-based feature
sets (such as those based on Gabor filter or quadrature mirror filter (QMF)).
Ciochetto, G.; R. Polidoro (1998). Site investigation and output
of utilities map using GPR. CSELT Technical Reports, 26,
(2): 177-84.
Keywords: Cartography; Object detection; Public utilities; Radar
imaging; Soil; Telecommunication cables; Telecommunication networks; Underground
cables; Site investigation; Utilities map; Subsoil; Underground network
infrastructures; Ground penetrating radar; 2D images; cselt; Torino; Digital
cartography ; Urban area
Original Abstract: An exhaustive knowledge of the subsoil right from
the first design phases of the construction of underground network infrastructures
is essential, both to limit possible damage to pre-existing utilities and
to reduce the number of failures especially if new trenchless techniques
are used. The most popular method for locating underground utilities is
definitely ground penetrating radar (GPR), due to its rapidity of execution,
good-quality results, and capacity to supply 2D images of the subsoil.
The paper presents the experimental results obtained by CSELT from some
field tests performed in the city of Torino using GPR and reports the data
obtained from the investigations of a digital cartography of the urban
area.
Clark, G. A.; S. K. Sengupta; W. D. Aimonetti; F. Roeske; J. G. Donetti
(2000). Multispectral image feature selection for land mine detection.
IEEE Transactions on Geoscience and Remote Sensing, 38, (1,
pt.1): 304-11.
Keywords: Buried object detection; Feature extraction; Geophysical
signal processing; Geophysical techniques; Image processing; Military systems;
Multidimensional signal processing; Remote sensing; Terrain mapping; Geophysical
measurement technique; Landmine; Military system; Multispectral remote
sensing; Optical imaging; Visible region; Infrared imaging; IR method;
Image feature selection; Land mine detection; Camera; Registered image;
Supervised-learning algorithm; Metal; Plastic land mine; Detection performance
; Land surface
Original Abstract: The authors' system uses a camera that acquires
registered images in six spectral bands and a supervised-learning algorithm
to detect metal and plastic land mines. Results show that even with a small
sample size, the detection performance is good and holds promise for future
work with larger data sets.
Clausi, D. A.; M. E. Jernigan (1998). A fast method to determine
co-occurrence texture features. IEEE Transactions on Geoscience
and Remote Sensing, 36, (1): 298-300.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image texture; Remote sensing; Geophysical measurement technique;
Land surface; Terrain mapping; Image processing; Image feature; Fast method;
Co-occurrence texture feature; Grey-level co-occurrence matrices; Occurrence
matrix; Linked-list algorithm ; Grey-level quantization
Original Abstract: A critical shortcoming of determining texture features
derived from grey-level co-occurrence matrices (GLCM's) is the excessive
computational burden. This paper describes the implementation of a linked-list
algorithm to determine co-occurrence texture features far more efficiently.
Behavior of common co-occurrence texture features across difference grey-level
quantizations is investigated.
Collischonn, W.; J. V. Pilar (2000). A direction dependent least-cost-path
algorithm for roads and canals. International Journal of Geographical
Information Science, 14, (4): 397-406.
Keywords: Dynamic programming; Geographic information systems; Transportation;
Visual databases; Direction dependent least-cost-path algorithm; Route
planning; Roads; Canals; Topography; Grid structure; Raster structure;
Geographical information systems ; Cost functions
Original Abstract: In planning routes for roads and canals, topography
is often a significant constraint. Among the infinite number of possible
trajectories between two points, the selected path should be a good approximation
to the one with the least cost, and should avoid extremes of slopes. In
the case of a canal, the number of uphill reaches of the trajectory should
be minimised. This paper presents a least-cost-path algorithm developed
to find the best path given the topography, the start and end-points of
the linear feature (canal or road) and a function relating slope, distance
and cost. The algorithm is based on dynamic programming techniques adapted
to solve problems on the grid, or raster structure usually used in geographical
information systems. The algorithm was programmed and used to solve hypothetical
problems. Although real cost functions were not used, the results were
coherent and showed the algorithm's capabilities.
Cometti, E.; E. Mozzi; R. Bardoscia; G. Parma; L. Ratti, (1996). GIS
as support for transport planning in Lombardia. Geographical Information
from Research to Application Through Cooperation. Second Joint European
Conference and Exhibition Proceedings of Joint European Conference on Geographical
Information Barcelona, Spain 27-29 March 1996
Amsterdam, Netherlands IOS Press, pp.933-43 vol.2.
Keywords: Cartography; Environmental factors; Geographic information
systems; Public administration; Town and country planning; Transportation;
Geographic information system; Transport planning; Transportation Department;
Territorial and Cartographic Information Office; Italy Regione Lombardia;
Territorial information system; System development project; Road network;
Rail network; Model assignment; Public transport; Private transport; Infrastructural
graph; Regional technical map; Regional officer ; Environmental analysis
Original abstract: The Transportation Department and the Territorial
and Cartographic Information Office of Regione Lombardia (Italy), within
a territorial information system (TIS) development project, carried out
the integration of the road and rail networks (as defined by the Transportation
Department) for model assignment of public and private transport, and produced
the infrastructural graph of the TIS, based on a regional technical map
with a 1:10,000 scale. This fundamental experience is a foundation for
supporting the regional officer for transport planning and programming
based on territorial and environmental analyses.
Console, E.; M. C. Mouchot, (1996). Fuzzy classification techniques
in the urban area recognition. IGARSS '96. 1996 International Geoscience
and Remote Sensing Symposium. Remote Sensing for a Sustainable Future (Cat.
No.96CH35875) Lincoln, NE, USA 27-31 May 1996
New York, NY, USA IEEE, pp.1373-5 vol.2.
Keywords: Fuzzy logic; Geophysical signal processing; Geophysical
techniques; Image classification; Image recognition; Image segmentation;
Remote sensing; Geophysical measurement technique; Land surface; Terrain
mapping; Multispectral remote sensing; Optical imaging; Visible; Infrared;
Satellite remote sensing; Fuzzy image classification; Urban area recognition;
Town; City; Fuzzy logic method; Landsat TM; Catanzaro; Calabria; Italy;
Fuzzy parallelepiped classifier ; Membership value
Original abstract: Determination of scattered urban areas in a very
heterogeneous environment can prove to be quite difficult using conventional
classification techniques of remotely sensed images. On the other hand,
fuzzy logic methods enable this difficulty to be overcome by assigning
one pixel to more than one class according to a membership grade, determined
using a pre-defined function. In this study, urban areas have been classified
using fuzzy logic methods. The analysis was performed on a Landsat TM sub-scene
(800*600 pixels) acquired over the province of Catanzaro (Calabria, Italy).
The intrinsic characteristics of the ground coverage, as well as the rough
topography, contribute to make this area a very heterogeneous one. The
image was classified using a fuzzy parallelepiped classifier and membership
values, associated to each pixel, were calculated. For each pixel, the
classes, which contributed the most, were kept for the determination of
the final pixel assignment. Global accuracy of fuzzy classification, estimated
on mixed test area (chosen during a 2/sup nd/ ground truth campaign) reached
a level of 0.75 Urban areas were identified analysing the images that represent
the combinations of "Urban" class with the other classes. The fuzzy classification
results were compared to image classified using the traditional techniques,
minimum distance and maximum likelihood. In terms of global accuracy, fuzzy
technique appeared to be more accurate than conventional techniques.
Console, E.; B. Solaiman, (2000). Problems and perspectives in the
high resolution data fusion. IGARSS 2000. IEEE 2000 International Geoscience
and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role
of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120)
Honolulu, HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, pp.2605-7 vol.6.
Keywords: Edge detection; Geophysical signal processing; Geophysical
techniques; Image processing; Image resolution; Multidimensional signal
processing; Remote sensing; Sensor fusion; Terrain mapping; Geophysical
measurement technique; Multispectral remote sensing; Data fusion; Land
surface; Optical imaging; Visible; ir; Infrared; High resolution; Multispectral
data; Standard merging method; Transformation; rgb; ihs; Algorithm ; Fuzzy
theory
Original abstract: The authors' attention has been focused on the prospects
held by the introduction of high resolution. They have particularly tried
to point out the difficulties and the advantages met in the fusion of high
resolution and multispectral data through a simple application carried
out in two phases. The first was aimed at the fusion of the data by means
of a standard merging method based on the transformation RGB-IHS and the
second was aimed at edge detection on the merged data by means of an algorithm
based on fuzzy theory.
Coombs, J.; M. Shasko, (1995). The integration of highway data into
the transportation centreline network. Ninth Annual Symposium on Geographic
Information Systems in Natural Resources Management. Symposium Proceedings
Proceedings Ninth Annual Symposium on Geographic Information Systems Vancouver,
BC, Canada 27-30 March 1995
Fort Collins, CO, USA GIS World, pp.277-8 vol.1.
Keywords: Cartography; Data handling; Geographic information systems;
Government data processing; Public administration; Transportation; Highway
data integration; Transportation centreline network; Planning Services
Branch; Ministry of Transportation and Highways; Road data; Transportation
data; Geographic information system; Digital map base; Disparate data sets;
Databases; Spatial based applications; Business needs; Rapidly evolving
service organization ; Testing
Original abstract: The Planning Services Branch of the Ministry of
Transportation and Highways has been actively developing strategies to
integrate road and transportation data into a geographic information system.
The efforts to date have included the construction of a digital map base
and the migration of several disparate data sets onto the map base. The
remaining task and challenge for the Branch is to move beyond the presentation
of data to the development of tools and mechanisms to evaluate, use, and
maintain these data bases for their clients. Increasing demands on the
Branch to provide and assimilate data in a timely manner has been the motivating
force behind these efforts to streamline data handling. It is anticipated
that through efforts to provide data in a unified system, the development
of spatial based applications is better served. The primary focus of the
presentation is to report on the development and testing of mapping applications
that meet the business needs of a rapidly evolving service organization.
Cord, M.; D. Declercq (2001). Three-dimensional building detection
and modeling using a statistical approach. IEEE Transactions on
Image Processing, 10, (5): 715-23.
Keywords: Bayes methods; Cartography; Feature extraction; Image
recognition; Image reconstruction; Image resolution; Image segmentation;
Monte Carlo methods; Parameter estimation; Remote sensing; Statistical
analysis; Stereo image processing; Stochastic processes; Three-dimensional
building detection; Statistical approach; High-resolution stereoscopic
aerial imagery; Hierarchical strategy; Urban sites; Global focusing; Local
modeling; Depth information; Adaptive correlation stereo matching; Multiplane
model; Mixture model; Bayesian approach; Augmentation; Stochastic algorithms
; Monte Carlo study
Original Abstract: In this paper, we address the problem of building
reconstruction in high-resolution stereoscopic aerial imagery. We present
a hierarchical strategy to detect and model buildings in urban sites, based
on a global focusing process, followed by a local modeling. During the
first step, we extract the building regions by exploiting to the full extent
the depth information obtained with a new adaptive correlation stereo matching.
In the modeling step, we propose a statistical approach, which is competitive
to the sequential methods using segmentation and modeling. This parametric
method is based on a multiplane model of the data, interpreted as a mixture
model. From a Bayesian point of view the so-called augmentation of the
model with indicator variables allows using stochastic algorithms to achieve
both model parameter estimation and plane segmentation. We then report
a Monte Carlo study of the performance of the stochastic algorithm on synthetic
data, before displaying results on real data.
Corr, D. G., (1997). Coherent change detection for urban development
monitoring. IEE Colloquium on Radar Interferometry (Ref. No.1997/153)
London, UK 11 April 1997, pp.6/1-6.
Keywords: Image resolution; Radar imaging; Remote sensing by radar;
Spaceborne radar; Synthetic aperture radar; Coherent change detection;
Urban development monitoring; Missed events; Descending orbits; Ascending
orbits; Ionospheric effects; Resolution ; Coherence measurement window
Original abstract: Coherence measurements provide a tool with significant
potential for detecting change. To reduce the likelihood of missed events
data from both ascending and descending orbits should be used. Data from
ascending orbits (which is acquired at 22.00 hrs GMT) lead to higher coherence
values than that from the descending orbits (at 11.00 hrs GMT); possibly
as a result of ionospheric effects: however the data available were over
a restricted period. The resolution of ERS data imposes a fundamental limit
to the sensitivity of the change measurements through the size of the coherence
measurement window.
Couloigner, I.; T. Ranchin (2000). Mapping of Urban Areas: A Multiresolution
Modeling Approach for Semi-Automatic Extraction of Streets. Photogrammetric
Engineering and Remote Sensing, V66, (N7): 867-874.
Keywords: automatic feature extraction
Synopsis: A paper discussing an automated method for extracting roads
from high resolution (2m and higher) satellite imagery. Strips of streets
are divided into substructures (of varying scale) which can be modeled
using a wavelet transform.
Original Abstract: A new method to hierarchically extract urban road
networks from very high spatial resolution space-borne imagery (spatial
resolution of 2 m and higher) is presented. An explicit and generic model
of "streets" was developed according to a multiresolution analysis of images
associated with a wavelet transform. The method consists of two processing
steps the multiresolution extraction of edges of streets, and, if existing,
the multiresolution extraction of strips of streets. The extraction of
sides is achieved by the use of a multiscale representation of images,
and the extraction of strips is done by a modeling of distinct substructures
of streets at different characteristic scales achieved by the application
of the associated wavelet transform. The method would help cartographers
in their cartographic works in urban areas by a partial automation of tasks.
Couloigner, I.; T. Ranchin, (1998). Extraction of urban network from
high spatial resolution imagery using multiresolution analysis and wavelet
transform. Wavelet Applications in Signal and Imaging Processing VI
San Diego, CA, USA 22-23 July 1998
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.103-12.
Keywords: Cartography; Edge detection; Feature extraction; Image
resolution; Remote sensing; Wavelet transforms; Urban network; High spatial
resolution imagery; Multiresolution analysis; Wavelet transform; Quadrangular
urban road network; Streets; Hierarchical system; Characteristic scales;
A trous algorithm; Semi-automatic multiresolution processing; Photo-interpreters
; Airborne thematic mapper images
Original abstract: This paper presents a new method to extract, semi-automatically,
a quadrangular urban road network from high spatial resolution imagery.
A quadrangular network is generally composed of different classes of streets
in a hierarchical system. The developed method is based both on multiresolution
analysis and on the wavelet transform. The multiresolution analysis allows
a multiscale analysis of images and thus the extraction of the streets
in a class-by-class way. The wavelet transform enables the modeling of
information at different characteristic scales. In the problem, it allows
the extraction of the topography of streets. These two mathematical tools
are combined in the "a trous" algorithm. The application of this algorithm
to images of urban areas has been used to develop semi-automatic multiresolution
processing. This method will help photo-interpreters in their cartographic
work by a partial automation of tasks.
Couloigner, I.; T. Ranchin; V. P. Valtonen; L. Wald (1998). Benefit
of the future SPOT-5 and of data fusion to urban roads mapping. International
Journal of Remote Sensing, V19, (N8): 1519-1532.
Keywords:
Original Abstract: This article deals with the contribution of both
the future SPOT-5 (which will produce images with the same bands as the
existing SPOT 1-3 ones but with an improved spatial resolution) and a sensor
fusion method to urban mapping. The ARSIS concept (in French: Amelioration
de la Resolution Spatiale par Injection de Structures ) is used for sensor
fusion. It allows the improvement of spatial resolution of the multi-band
images, while preserving spectral information, by use of the high frequencies
of panchromatic images. A well-proven method for urban mapping is then
applied to all multi-spectral images available in the context of the study.
A photo-interpretation of the latter confirms the benefit of fine image
resolutions to urban roads mapping, in the limit of the sensor studied
here. Then, when comparing the roads surface at all resolutions with reference
extracted from accurate maps of the city, we demonstrate quantitatively
that the finer the resolution, the more accurate the cartography.
Coulter, L.; D. Stow; B. Kiracofe; C. Langevin; D. M. Chen; S. Daeschner;
D. Service; J. Kaiser (1999). Deriving current land-use information
for metropolitan transportation planning through integration of remotely
sensed data and GIS. Photogrammetric Engineering and Remote Sensing,
V65, (N11): 1293-1300.
Keywords: Optics/Acoustics
Cowdery, J. M.; J. L. Kurtz, (1999). Ground penetrating radar signal
processing techniques for road subsurface measurements. Radar Sensor
Technology IV Orlando, FL, USA 8 April 1999
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.86-94.
Keywords: Graphical user interfaces; Radar computing; Radar signal
processing; Radar tracking; Road vehicle radar; Signal classification;
Road subsurface measurements; Time domain data; Road subsurface characterization;
Florida Department of Transportation; High-resolution ground penetrating
radar; Test van; Road surface thickness; Subsurface layer thickness; Voids;
Road layer interfaces; University of Florida; Road subsurface layer interface
detection; Road subsurface layer interface tracking; Road subsurface layer
interface joining; Subsurface layer classification; Computational fast
layer tracking processing; Research; Road subsurface conditions; Road design;
Road improvement ; Graphical user interface
Original abstract: Ground penetrating radar (GPR) has become a recognized
tool for road subsurface characterization. The Florida Department of Transportation
(FDOT) performs road subsurface measurements throughout Florida with a
high-resolution ground penetrating radar mounted on a test van traveling
at speeds up to 55 MPH. The time domain data collected by the GPR allow
the determination of thickness of the road surface and subsurface layers.
With appropriate signal processing, the data can provide some insights
about voids and other anomalies near road layer interfaces. The University
of Florida, as part of a project for the FDOT, has developed a signal processing
technique for detecting, tracking and joining, and analyzing road subsurface
layer interfaces. This paper describes the novel techniques and results
of the current project to employ advanced signal processing techniques
to detect and classify subsurface layers. In particular, a computational
fast layer tracking processing technique is described along with results
of the algorithm. It is expected that the ground penetrating radar and
the results of current research will assist the FDOT in determining more
accurate road layer thickness profiles, assessing road subsurface conditions
with less coring, and rehabilitating roads with less manpower than is now
required. Such capabilities will allow potentially serious problems to
be corrected before they become costly and will also provide a useful tool
for future road design and improvement.
Croteau, K.; D. Skiles; B. Faber (1997). Custom GIS revamps Denver's
former airport. GIS World, 10, (6): 50-2.
Keywords: Geographic information systems; Public administration;
Custom GIS; Denver Stapleton International Airport, CO, USA; Site redevelopment;
Sustainability; Commercial areas; Residential areas; Natural areas; Stapleton
Redevelopment Foundation; Land-use plan; Planning expertise; Cost; Land-use
balance; Water use; Solid waste generation; Wastewater generation; Transportation;
Energy consumption; Denver Smart Places Project; GIS-based land-use decision
support system; smart places; ESRI Inc.; ArcView; Consortium for International
Earth Science Information Network; Active Response GIS; Interactive land-use
design ; Interactive infrastructure specification
Original Abstract: The site of the former Stapleton International Airport
at Denver, CO, USA contains wildlife habitat and buildings destined for
demolition. Redevelopment efforts focus on sustainability and better integration
of commercial, residential and natural areas. The Stapleton Redevelopment
Foundation (SRF) was formed to consider an array of development ideas and
to define a final comprehensive land-use plan. SRF had access to two important
resources: planning expertise and the use of an extensive GIS database
covering the Stapleton site, but SRF didn't have a methodology for combining
its expertise and GIS data into an efficient system for creating, modifying
and comparing alternative land-use plans in terms of cost, land-use balance,
water use, solid waste and wastewater generation, transportation and energy
consumption. The need for a comprehensive methodology engendered the nonprofit
Denver Smart Places Project and a GIS-based land-use decision support system
called SMART PLACES. The system, built on ESRI Inc.'s ArcView platform
and the Consortium for International Earth Science Information Network's
Active Response GIS, emphasizes interactive land-use design and infrastructure
specification using a hands-on, what-if approach.
Curran, P. J.; J. L. Dungan (1990). An Image Recorded by the Airborne
Visible Infrared Imaging Spectrometer (Aviris). International Journal
of Remote Sensing, V11, (N6): 929-931.
Keywords:
Curran, P. J.; J. L. Dungan (1989). Estimation of Signal-to-Noise
- a New Procedure Applied to Aviris Data. Ieee Transactions on Geoscience
and Remote Sensing, V27, (N5): 620-628.
Keywords: AVIRIS , signal to noise ratio, intrapixel variability
Original Abstract: To make the best use of narrowband Airborne Visible/Infrared
Imaging Spectrometer (AVIRIS) data, an investigator needs to know the signal-to-noise
ratio (SNR). The signal is land cover dependent and varies with both wavelength
and atmospheric absorption, and random noise comprises sensor noise and
intrapixel variability (i.e. variability within a pixel). The three existing
methods for estimating the SNR are inadequate, since typical laboratory
methods inflate, while typical dark-current and image methods deflate the
SNR value. The authors propose a procedure called the geostatistical method
that is based on the removal of periodic noise by notch filtering in the
frequency domain and the isolation of sensor noise and intrapixel variability
using the semivariogram. This procedure was applied easily and successfully
to five sets of AVIRIS data from the 1987 flying season and could be applied
to remotely sensed data from broadband sensors.
Czerniak, R. J.; J. P. Reilly; National Cooperative Highway Research
Program; National Research Council (U.S.). Transportation Research Board;
American Association of State Highway and Transportation Officials; United
States. Federal Highway Administration (1998). Applications of GPS for
surveying and other positioning needs in departments of transportation.
Washington D.C., National Academy Press.
Dabis, H.; P. Palmer; J. Kittler, (1995). An interest operator based
on perceptual grouping. Theory and Applications of Image Analysis II.
Selected Paper from the 9th Scandinavian Conference on Image Analysis Uppsala,
Sweden June 1995
Singapore World Scientific, pp.211-24.
Keywords: Computer vision; Feature extraction; Object recognition;
Remote sensing; Interest operator; Perceptual grouping; Aerial scenes;
Nonaccidentalness; Region information ; Focus of attention
Original abstract: In this paper, perceptual grouping is used to assign
interest levels to complex structures in images of aerial scenes. The interest
operator increases exponentially as more features belonging to the structure
are detected. We apply the theory of non-accidentalness to focus our attention
on events which are least likely to occur anywhere in the image except
for the structure we are detecting. Region information obtained in the
first sweep is fed back to the earlier levels of processing to improve
the performance of feature extraction. We use the approach to detect bridges
and runways although the ideas developed could be applied to any other
complex structures. Only a small number of hypotheses are generated and
results presented show that, in most cases, the interest levels for regions
containing the structures are much higher than those for other regions.
It is also shown that using the focus of attention improves the performance
of feature extraction.
Dammert, P. B. G.; J. I. H. Askne; S. Kuhlmann, (1999). Unsupervised
segmentation of multitemporal interferometric SAR images. IGARSS '98.
Sensing and Managing the Environment. 1998 IEEE International Geoscience
and Remote Sensing. Symposium Proceedings Seattle, WA, USA 6-10 July 1998,
pp.2259-71.
Keywords: Adaptive signal processing; Feature extraction; Geophysical
signal processing; Geophysical techniques; Image classification; Image
segmentation; Image sequences; Radar imaging; Remote sensing by radar;
Synthetic aperture radar; Terrain mapping; Geophysical measurement technique;
Land surface; Radar remote sensing; Unsupervised segmentation; Multitemporal
image; Image sequence; Interferometric SAR image; InSAR; Fuzzy clustering
method; Adaptive feature extraction; Principal component transformation;
Fuzzy clustering; Iteration; Urban area; Forest; Farmland; Dominant land-cover
; Rule-based method
Original abstract: This paper shows how to segment large data sets
of multitemporal and interferometric SAR images using an unsupervised,
fuzzy clustering method. An adaptive feature extraction (principal component
transformation) is employed which may drastically reduce the number of
images and improves the final results. This also speeds up the fuzzy clustering
iteration part considerably. The method is applied to data over two areas
in Sweden: one typical urban area with forest and farmland surroundings
and a forested area. The best classification accuracy is obtained when
classifying the data into two classes, agreeing with the predictions of
the cluster validity parameters used in this study. The method always finds
the dominating land-covers in the images first. These are then subdivided
as more clusters (classes) are identified, indicating that the segmentation
is moderately hierarchical. The final classification results, between 65%
and 75%, are comparable to those obtained in other studies. Analyzing the
final cluster signatures reveals that the current unsupervised method has
several similarities with rule-based methods.
Dantas, A.; K. Yamamoto; M. V. Lamar; Y. Yamashita, (2000). Neural
network for travel demand forecast using GIS and remote sensing. Proceedings
of the IEEE-INNS-ENNS International Joint Conference on Neural Networks.
IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New
Millennium Como, Italy 24-27 July 2000
Los Alamitos, CA, USA IEEE Comput. Soc, pp.435-40 vol.4.
Keywords: Feedforward neural nets; Geographic information systems;
Multilayer perceptrons; Town and country planning; Transportation; Travel
demand forecast; gis; Transportation planning; Urban area; Land use-transportation
system interaction; Remote sensing images; Geographical information system
; Boston metropolitan area
Original abstract: Describes an application of neural networks in the
development of a travel forecast model for transportation planning. The
model intends to quantify trips within the urban area through the representation
of the land use-transportation system interaction. The data to express
such a complex interaction is mainly obtained from remote sensing images
that are processed in a geographical information system. We present the
model's basic formulation and the results of a case study conducted in
the Boston metropolitan area.
Dare, P. M.; I. J. Dowman, (2000). Automatic registration of SAR
and SPOT imagery based on multiple feature extraction and matching.
IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium.
Taking the Pulse of the Planet: The Role of Remote Sensing in Managing
the Environment. Proceedings (Cat. No.00CH37120) Honolulu, HI, USA 24-28
July 2000
Piscataway, NJ, USA IEEE, pp.2896-8 vol.7.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image registration; Radar imaging; Remote sensing; Remote sensing
by radar; Sensor fusion; Spaceborne radar; Synthetic aperture radar; Terrain
mapping; Geophysical measurement technique; Land surface; Optical imaging;
Radar remote sensing; sar; Automatic registration; SPOT imagery; Multiple
feature extraction; Feature matching ; Satellite remote sensing
Original abstract: Many different models have been developed in the
past to automatically register SAR and optical images. The vast majority
of these models rely on feature based matching, due to the very different
backscattering properties of the terrain in the optical and microwave regions
of the electromagnetic spectrum. Even so, the difficulties associated with
extracting similar features from radiometrically very different images
have always hindered this approach to automatic registration. The model
proposed in this paper uses feature based matching, but rather than relying
on just one method of feature extraction, many different feature extraction
algorithms are employed. This methodology ensures there is a large set
of features extracted from each image to be matched. Consequently the chances
of locating pairs of correctly matched points, which can be used in either
a polynomial or a photogrammetric rectification model, are greatly increased.
Application of the proposed algorithm to pairs of both small and large
images showed that a substantial number of tie points could be accurately
located in each pair of images. More importantly, the approach to feature
based registration using multiple feature extraction techniques clearly
improved the quality and quantity of the tie points compared to traditional
feature based registration techniques which rely on only one feature extraction
algorithm.
Dare, P. M.; C. S. Fraser, (2000). Linear infrastructure mapping
using airborne video imagery and subsequent integration into a GIS.
IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium.
Taking the Pulse of the Planet: The Role of Remote Sensing in Managing
the Environment. Proceedings (Cat. No.00CH37120) Honolulu, HI, USA 24-28
July 2000
Piscataway, NJ, USA IEEE, pp.2299-301 vol.5.
Keywords: Feature extraction; Geographic information systems; Image
segmentation; Image sequences; Power distribution lines; Remote sensing;
Video signal processing; Linear infrastructure mapping; Airborne video
imagery; GIS integration; Spatial data; Helicopter based digital video
sensors; High resolution colour imagery; Australia; Powerlines; Vector
data; Ground measurements; Spatial information database; Power distribution
network; Downward looking video sequences; Feature based matching; Image
mosaics; Pylons; Ground control points; Georeferenced strip maps; Powerline
network; Digital topographic data; Terrain ; Video image mosaicing
Original abstract: Airborne video imagery is an ideal tool for acquiring
the spatial data needed for mapping linear infrastructure features. The
flexibility offered by helicopter based digital video sensors means that
high resolution colour imagery can be acquired of long linear features
quickly and cheaply. This paper describes how airborne video has been implemented
in Australia to map powerlines, and subsequently how the map products have
been integrated with vector data and ground measurements to create a detailed
spatial information database of the power distribution network. Strip maps
are created from downward looking video sequences by mosaicing captured
images using a combination of ephemeris data with area and feature based
matching. Absolute orientation of the image mosaics is carried out interactively
using the locations of the pylons as ground control points. An assessment
of the resulting mosaics showed that the accuracy of the rectification
of the image sequences was acceptable for purposes of this project. The
georeferenced strip maps were imported into a GIS, the design of which
allows the operator to view an image map of any part of the entire powerline
network with digital topographic data overlaid. Selecting particular points
on the image map provides access to information such as tower type, location
and height. In addition, the operator is able to view a movie clip of any
part of the powerline and surrounding terrain, from either a downward or
forward looking perspective.
de Oliveira, M. G. S.; P. C. M. Ribeiro (2001). Production and analysis
of coordination plans using a geographic information system. Transportation
Research Part C (Emerging Technologies), 9C, (1): 53-68.
Keywords: Geographic information systems; Road traffic; Timing;
Traffic control; Transportation; TRANSYT program; Signal coordination plans;
SIGTRAF system; GIS-T technology; Topological information; Thematic mapping
capabilities ; Timing plans
Original Abstract: The TRANSYT program is one of the most extensively
used programs for the production of signal coordination plans. The impediments
to the development of signal coordination plans are associated with data
collection and data input. GIS offers a natural solution to these problems.
The paper presents the SIGTRAF system, which uses GIS-T technology for
the production of coordination plans using TRANSYT. This system is able
to extract topological information from the GIS-T, thus simplifying the
process of coding TRANSYT models. A case study was performed, providing
insight on how the GIS-T's thematic mapping capabilities can be used to
visually compare different timing plans.
De Vore, R. A.; W. Shao; J. F. Pierce; E. Kaymaz; B. T. Lerner; W. J.
Campbell, (1997). Using nonlinear wavelet compression to enhance image
registration. Wavelet Applications OV Orlando, FL, USA 22-24 April
1997
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.539-51.
Keywords: Data compression; Feature extraction; Image coding; Image
enhancement; Image matching; Image registration; Remote sensing; Transform
coding; Wavelet transforms; Nonlinear wavelet compression; Complexity analysis;
High-level compression; Control point extraction; Control point matching;
Point alignment technique ; Landsat TM image
Original abstract: We present a method for automatically registering
images based on nonlinear compression. The method involves three steps:
(i) analysis of the complexity of the images; (ii) high-level compression
for extracting control points in the images; (iii) registration of the
images by matching control points. The first step analyzes the complexity
of the given images. It numerically computes from any image a complexity
index which determines the efficiency at which the image can be compressed.
This index is used in the second step of the algorithm to select coefficients
in the wavelet representation of the image to produce a highly compressed
image. The wavelet coefficients of the highly compressed image are then
transformed to pixel values. Only a few pixel values (called control points)
are nontrivial. The third stage of the algorithm uses a point alignment
technique to identify matching control points and to erect the registering
transformations. The algorithm is tested on two quite different scenes:
a portrait, representing an uncomplicated scene, and a Landsat TM image
of the Pacific Northwest. In both cases, images are tested which differ
by a rotation and which differ by a rigid transformation. The algorithm
allows a choice of different metrics in which to do the compression and
selection of control points.
Del Frate, F.; J. Lichtenegger; D. Solimini, (1999). Monitoring urban
areas by using ERS-SAR data and neural networks algorithms. IEEE 1999
International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat.
No.99CH36293) Hamburg, Germany 28 June-2 July 1999
Piscataway, NJ, USA IEEE, pp.2696-8 vol.5.
Keywords: Geography; Image classification; Image texture; Neural
nets; Radar imaging; Remote sensing by radar; Spaceborne radar; Synthetic
aperture radar; Urban areas; ERS-SAR data; Neural networks algorithms;
Multitemporal SAR data; Classification; Rome; Italy; Coherence; Textural
features; SAR images; Winter; Spring; Summer; ERS tandem mission; Water
surfaces; Woodland; Parks; Residential areas ; Decision-making process
Original abstract: This contribution discusses the kind of information
contained in multitemporal SAR data and shows how it can be exploited for
classifying the urban area of Rome, Italy. Multitemporal, coherence and
textural features are obtained from a set of SAR images taken in winter,
spring and summer by the ERS tandem mission. These features are used to
identify areas belonging to various urban classes, including water surfaces,
woodland and parks, and continuous high/low density residential areas.
The decision-making process is performed by a classifier based on a neural
network algorithm.
Del Frate, F.; A. Petrocchi; J. Lichtenegger; G. Calabresi, (2000).
Neural networks for oil spill detection using ERS-SAR data. IEEE
1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 Hamburg,
Germany 28 June-2 July 1999
Ieee
IEEE Trans. Geosci. Remote Sens. (USA), pp.2282-7.
Keywords: Feature extraction; Geophysical signal processing; Geophysics
computing; Image classification; Neural nets; Oceanographic techniques;
Radar imaging; Remote sensing by radar; Water pollution measurement; Water
pollution; Marine pollution; Oil spill; Oil slick; Radar remote sensing;
Measurement technique; Neural network; Neural net; ers; sar; Spaceborne
radar; Semi-automatic detection; Algorithm; Extended pruning procedure
; Image processing
Original abstract: A neural network approach for semi-automatic detection
of oil spills in European remote sensing satellite-synthetic aperture radar
(ERS-SAR) imagery is presented. The network input is a vector containing
the values of a set of features characterizing an oil spill candidate.
The classification performance of the algorithm has been evaluated on a
data set containing verified examples of oil spill and look-alike. A direct
analysis of the information content of the calculated features has been
also carried out through an extended pruning procedure of the net.
Dell'Acqua, F.; P. Gamba (2001). Detection of urban structures in
SAR images by robust fuzzy clustering algorithms: The example of street
tracking. Ieee Transactions on Geoscience and Remote Sensing,
V39, (N10): 2287-2297.
Keywords: street extraction Fuzzy clustering, urban remote sensing.
Original Abstract: In this work, we present a fuzzy approach to the
analysis of airborne synthetic aperture radar (SAR) images of urban environments.
In particular, we want to show how to implement structure extraction algorithms
based on fuzzy clustering unsupervised approaches.To this aim, the idea
is to segment first the sensed data and recognizen very basic urban classes
(vegetation, roads, and built areas). Then, from these classes, we extract
structures and infrastructures of interest. The initial clustering step
is obtained by means of fuzzy logic concepts and the successive analyses
are able to exploit the corresponding fuzzy partition. As a possible complete
procedure for urban SAR images, in this paper, we focus on the street tracking
and extraction problem. Three road extraction algorithms available in literature
(namely, the connectivity weighted Hough transform (CWHT), the rotation
Hough transform, and the shortest path extraction) have been modified to
be consistent with the previously computed fuzzy clustering results. Their
different capabilities are applied for the characterization of streets
with different width and shape. The whole approach is validated by the
analysis of AIRSAR images of Los Angeles, CA.
Dell'Acqua, F.; P. Gamba (2001). Query-by-shape in meteorological
image archives using the point diffusion technique. IEEE Transactions
on Geoscience and Remote Sensing, 39, (9): 1834-43.
Keywords: Atmospheric techniques; Clouds; Feature extraction; Geophysical
signal processing; Geophysics computing; Image retrieval; Meteorology;
Query formulation; Cloud; Image archive; Image processing; Query-by-shape;
Point diffusion; Point diffusion technique; Remote sensing; Measurement
technique; Shape similarity evaluation; Querying; Database query; Retrieval
speed; Precision; Shape feature; Image feature ; Searching
Original Abstract: The authors work on meteorological satellite image
archives and provide a novel and useful query-by-shape tool. To this aim,
they first present the point diffusion technique (PDT), a fast and efficient
method for shape similarity evaluation. Thanks to its very structure, this
approach is suitable to handle objects whose shape is not well defined
and can be represented by a set of sparse points. PDT is thus suitable
for application to similarity-based retrieval from remotely sensed image
archives, where shapes are hardly defined but are still among the major
features of interest. Moreover, they prove here that PDT is almost as effective
as more standard procedures for shape-based database queries, although
significantly faster. In other words, it manages to combine retrieval speed
and precision, the features of greatest importance for a first remote sensing
data prescreening in many applications. Archives of meteorological satellite
images are typical examples of very large-sized, remote sensing-based databases
with a special attention for shape features. Each meteorological satellite
produces terabytes of data every day, a large part of which is not immediately
analyzed and ends being stored in archives. The application of PDT to such
a database is presented and discussed, and a comparison with a standard
method developed for meteorological shape analysis is provided.
Dell'Acqua, F.; P. Gamba; B. Houshmand, (1998). Recognition of urban
structures in multiband data by means of ART networks. IGARSS '98.
Sensing and Managing the Environment. 1998 IEEE International Geoscience
and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174) Seattle,
WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.400-2 vol.1.
Keywords: Adaptive signal processing; ART neural nets; Geophysical
signal processing; Geophysical techniques; Geophysics computing; Image
classification; Image recognition; Remote sensing; Sensor fusion; Geophysical
measurement technique; Land surface; Terrain mapping; Multispectral remote
sensing; Image processing; Urban structure; Multiband data; ART network;
Neural net; Neural network; Multiband image; Adaptive resonance theory;
Town; City; Spatial analysis; Spectral analysis; Clustering step; Class
redundancy; SAR image; Santa Monica; California; usa; Los Angeles; Radar
remote sensing ; Image fusion
Original abstract: Multiband images of a urban environment are analyzed
and interpreted by means of a neural network approach. In particular, the
advantages found by using adaptive resonance theory networks both for a
spatial and spectral analysis of the data are shown and commented. Moreover,
the authors simplify existing similar approaches by introducing a clustering
step that automatically solves the problem of class redundancy, typical
of the ART classification output. Results are given for a photo+SAR image
of Santa Monica, Los Angeles.
Dell'Acqua, F.; P. Gamba; A. Mecocci, (1997). Image database retrieval
by means of sketches and modal matching. Proceedings of 6th International
Conference on Image Processing and its Applications (Conf. Publ. No.443)
Dublin, Ireland 14-17 July 1997
London, UK IEE, pp.96-100 vol.1.
Keywords: Database theory; Image matching; Image sampling; Query
processing; Visual databases; Image database retrieval; Modal matching;
Visual search; User-defined sketch; Similarity evaluation; Scarcely sampled
shapes; Sampled objects ; Similarity indexes
Original abstract: We present the application of different indexes
based on the modal matching technique to visual search in an image database.
The problem of efficiently retrieving the images similar to a user-defined
sketch is addressed. Similarity evaluation for scarcely sampled shapes
is outlined, as well as the problems related to modal matching between
differently sampled objects. To these aims, four different definitions
of suitable similarity indexes are introduced and discussed.
Deloukas, A.; I. Kokkinos; G. Kiousis; D. Zannou, (1997). GIS-based
transportation planning and analysis: a practical implementation. Transportation
Systems 1997. (TS'97). Proceedings volume from the 8th IFAC/IFIP/IFORS
Symposium Proceedings of the 8th IFAC/IFIP/IFORS. Transportation Systems
1997 (3 vol.) Chania, Greece 16-18 June 1997
Oxford, UK Pergamon, pp.417-26 vol.1.
Keywords: Cartography; Data analysis; Entity-relationship modelling;
Geographic information systems; Service industries; Strategic planning;
Transportation; Visual databases; Transportation planning; Geographic information
system; gis; Metro Development Study; Data management; Conceptual design;
Geographical database; Public transport routes; Geocoding application;
Data display; Thematic maps; Data manipulation; Map overlays ; Spatial
data analysis
Original abstract: The potential of using advanced geographic information
system (GIS) technology in transportation is illustrated in the context
of the Metro Development Study (MDS). Data management issues related to
the conceptual design of the MDS geographical database and the representation
of public transport routes are discussed. A geocoding application serving
the needs of the transportation model is described. Examples of data display
(thematic maps) and data manipulation (map overlays) are given, and findings
of spatial data analysis are presented. The overview of GIS-based transportation
applications within MDS illustrates the advantages of integrating both
systems.
Demin, X. (2000). A three-stage computational approach to network
matching. Transportation Research Part C (Emerging Technologies),
8C, (1-6): 71-89.
Keywords: Geographic information systems; Image matching; Marine
systems; Traffic information systems; Transportation; Visual databases;
Three-stage computational approach; Network matching; Data integration;
Manual manipulation; Three-stage matching algorithm; Node matching; Segment
matching; Edge matching; Top-down procedures; Matching computation; Sensitive
matching measures; Waterway networks; Matching rate; Computational efficiency;
Linear alignment; Aspatial matching; Higher-level matching; gis ; Transportation
networks
Original Abstract: Network matching is frequently needed for integrating
data that come from different sources. Traditional ways of finding correspondences
between transportation networks are time-consuming and require considerable
manual manipulation. The paper describes a three-stage matching algorithm
(node matching, segment matching, and edge matching) that combines bottom-up
and top-down procedures to carry out the matching computation. As it uses
sensitive matching measures, the proposed algorithm promises good improvement
to existing algorithms. An experiment of matching two waterway networks
is reported in the paper. The results of this experiment demonstrate that
a reasonable matching rate and good computational efficiency can be achieved
with this algorithm. The paper also briefly discusses necessary improvements
in areas of linear alignment, aspatial matching and higher-level matching.
Demirbilek, A.; H. Pastaci, (2000). The planning of solution to Istanbul's
transportation problem with the coordination of transportation subsystems
by the aid of GPS. SICE 2000. Proceedings of the 39th SICE Annual Conference.
International Session Papers (IEEE Cat. No.00TH8545) Proceedings of 39th
Annual Conference of the Society of Instrument and Control Engineers -
Japan (SICE 2000) Iizuka, Japan 26-28 July 2000
Tokyo, Japan Soc. Instrum. & Control Eng
SICE 2000. Proceedings of the 39th SICE Annual Conference. International
Session Papers (IEEE Cat. No.00TH8545)
Access restricted., pp.297-300.
Original abstract: The Istanbul Transportation Information System has
been established for the purpose of processing and storing data in a computer
environment to arrange, in its most general sense, management service,
coordination and planning activities related to transportation. The information
system for this objective is one that is composed of a series of hardware
and software components including data storage, data processing, data analysis
and representation functions. A database called the transportation information
system for evaluating the graphical and non-graphical data for the city
of Istanbul has been formed. Moreover, some analyses are performed toward
design and optimization based on a single intersection. Up-to-date data
are accessed with an appropriate interrogation easily and efficiently.
Deschenes, F.; D. Ziou, (2000). Extracting line junctions from curvilinear
structures. IGARSS 2000. IEEE 2000 International Geoscience and Remote
Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing
in Managing the Environment. Proceedings (Cat. No.00CH37120) Honolulu,
HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, pp.1672-4 vol.4.
Keywords: Feature extraction; Geophysical signal processing; Geophysical
techniques; Image processing; Remote sensing; Terrain mapping; Geophysical
measurement technique; Land surface; Line junction; Curvilinear structure;
Gray-level image; Algorithm; Local line curvature; Orientation vector;
Localization ; Road intersection
Original abstract: Describes an efficient approach for the detection
of line junctions in gray-level images. The algorithm is divided into two
steps. First, given the lines extracted from the original image, local
line curvature is estimated. For this purpose, two different measures of
curvature are proposed: The rate of change of direction of the orientation
vector along the line and the mean of the dot products of orientation vectors
within a given neighborhood. The second step involves the localization
of junctions. Examples are provided based on experiments with remotely
sensed images containing road intersections.
Descombes, X.; M. Sigelle; F. Preteux (1999). Estimating Gaussian
Markov random field parameters in a nonstationary framework: application
to remote sensing imaging. IEEE Transactions on Image Processing,
8, (4): 490-503.
Keywords: Feature extraction; Gaussian processes; Geophysical signal
processing; Image segmentation; Image texture; Least squares approximations;
Markov processes; Parameter estimation; Random processes; Remote sensing;
Gaussian Markov random field parameters; Nonstationary framework; Remote
sensing imaging; Textural parameters; Textural features; Nonstationarities;
Estimation methods; Conditional probabilities; Least square approximation;
Piecewise constant local mean; Blurring effect; Renormalization theory;
Cramer-Rao estimators; Sampling; Texture discrimination; Urban areas; SPOT
image ; Delineation
Original Abstract: In this paper, we tackle the problem of estimating
textural parameters. We do not consider the problem of texture synthesis,
but the problem of extracting textural features for tasks such as image
segmentation. We take into account nonstationarities occurring in the local
mean. We focus on Gaussian Markov random fields for which two estimation
methods are proposed, and applied in a nonstationary framework. The first
one consists of extracting conditional probabilities and performing a least
square approximation. This method is applied to a nonstationary framework,
dealing with the piecewise constant local mean. This framework is adapted
to practical tasks when discriminating several textures on a single image.
The blurring effect affecting edges between two different textures is thus
reduced. The second proposed method is based on renormalization theory.
Statistics involved only concern variances of Gaussian laws, leading to
Cramer-Rao estimators. This method is thus especially robust with respect
to the size of sampling. Moreover, nonstationarities of the local mean
do not affect results. We then demonstrate that the estimated parameters
allow texture discrimination for remote sensing data. The first proposed
estimation method is applied to extract urban areas from SPOT images. Since
discontinuities of the local mean are taken into account, we obtain an
accurate urban areas delineation. Finally, we apply the renormalization
based on method to segment ice in polar regions from AVHRR data.
Dherete, P.; J. Desachy, (2000). Cooperation and fusion of operators:
Application to automatic matching of cartographic objects. IGARSS 2000.
IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking
the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment.
Proceedings (Cat. No.00CH37120) IGARSS 2000. Honolulu, HI, USA 24-28 July
2000
Piscataway, NJ, USA IEEE, pp.2626-8 vol.6.
Keywords: Cartography; Feature extraction; Fuzzy logic; Geophysical
signal processing; Geophysical techniques; Image matching; Image processing;
Pattern matching; Remote sensing; Sensor fusion; Terrain mapping; Geophysical
measurement technique; Land surface; Data fusion; Operators; Automatic
matching; Cartographic object; SPOT image; Geographic database; Linear
feature; Geographic feature; Complex image; Context; Complexity; Scene
understanding; Diversity; Imprecision ; Fuzzy logic theory
Original abstract: The aim of this study is to provide a system able
to identify and to match linear cartographic objects on SPOT images using
geographic databases, and to update the databases. Automatic matching of
geographic features in complex remote sensing images faces different problems,
such as diversity of context or complexity of information. In order to
simplify identification and to limit the search space, the authors use
databases as a priori knowledge to help the scene understanding. But, diversity
and imprecision of information sources to process generate new problems.
The fuzzy logic theory has proved in the last years its ability to solve
a large range of problems concerning imprecision.
Dherete, P.; J. Desachy, (1998). Extraction of geographic features
using multi-operator fusion. Image and Signal Processing for Remote
Sensing IV Barcelona, Spain 21-23 Sept. 1998
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.418-28.
Keywords: Dynamic programming; Feature extraction; Fuzzy logic;
Geography; Geophysical signal processing; Image recognition; Remote sensing;
Sensor fusion; Geographic features; Multi-operator fusion; Remote sensing
images; Automatic analysis; Context diversity; Complexity of information;
Scene understanding; Fuzzy logic theory; Imprecision; Extraction algorithms;
Radiometry; Color; Linearity; Wrong detections; Generic models; Optimal
path; Linear feature; Snake-like technique ; Erratic parts
Original abstract: Automatic analysis of remote sensing images faces
different problems: context diversity, complexity of information. To simplify
identification and to limit the search space, we use extra data and knowledge
to help the scene understanding. Diversity and imprecision of information
sources generate new problems. The fuzzy logic theory is used to solve
the problem of imprecision. Many extraction algorithms are used to provide
a more reliable result. Extraction may be performed either globally on
the whole image or locally using information of data bases. Each extractor
produces a map of certainty factors for a given type of geographic features
according to their characteristics: radiometry, color, linearity, etc.
Maps contain wrong detections due to imperfections of the detectors or
non-completeness of generic models. So, we generate a new map using fusion
to have a best credibility used to compute a dynamic programming. It finds
an optimal path even if the linear feature is partially occluded. But the
path is generally erratic due to noise. Then a snake-like technique to
smooth the path to clean the erratic parts and to tune the level of detail
required to represent the geographic features on a map of a given scale
is given. The result is used to update data bases.
Di Carlo, W.; G. G. Wilkinson, (1996). Dominant linear feature detection
in satellite images using a self-organizing neural network. Proceedings
of the International Workshop on Soft Computing in Remote Sensing Data
Analysis Milan, Italy 4-5 Dec. 1995
Singapore World Scientific, pp.73-9.
Keywords: Feature extraction; Image segmentation; Remote sensing;
Self-organising feature maps; Dominant linear feature detection; Satellite
images; Self-organizing neural network; Automatic geometrical feature detection;
Geometrical rectification; Meaningful structure identification; Landscape;
Mapping; Kohonen maps; Dominant linear segment detection ; Segmented binary
images
Original abstract: The automatic detection of geometrical features
in satellite images is an important requirement in remote sensing both
for carrying out geometrical rectification and for identifying meaningful
structures on the landscape in mapping studies. The paper describes an
experimental study carried out at the Joint Research Centre to explore
the use of Kohonen maps to detect dominant linear segments in binary images
segmented from satellite images. This procedure forms part of a wider activity
concerned with carrying out geometrical rectification of images.
Ding, C. (1998). The GIS-based human-interactive TAZ design algorithm:
examining the impacts of data aggregation on transportation-planning analysis.
Environment and Planning B-Planning & Design, V25, (N4):
601-616.
Keywords:
Original Abstract: An aggregate approach (traffic analysis zones, TAZs)
has been used to conduct conventional transportation-planning analysis.
The impact of a TAZ (sizes and boundaries) on traffic demand estimates
and evaluation of transportation systems, however, has not been addressed
adequately in the literature. In this paper I will attempt to examine the
impact of spatial data aggregation on transportation by generating TAZ
alternatives and building linkages among land use, transportation, and
GIS. This paper consists of two major components. In the first, attention
is focused on the discussion of the GIS-based interface system which links
land use, transportation, and GIS. The GIS-based interface system also
includes a GIS-based human-interactive TAZ design algorithm that generates
TAZ alternatives. In the second, I concentrate on the examination of the
impact of TAZs on transportation. This is conducted by simulations, which
create TAZ alternatives and report final estimates of traffic demand and
evaluation of transportation systems. It is concluded that spatial data
aggregation affects the outcomes of transportation-planning models significantly,
particularly when the number of TAZs is small.
dos Santos Mendonca, P. R.; A. C. Frery, (1998). Genetic-annealing
parameter estimation for intensity SAR data. Second Latino-American
Seminar on Radar Remote Sensing. Image Processing Techniques (ESA SP-434)
Santos, Brazil 11-12 Sept. 1998
Noordwijk, Netherlands ESA, pp.37-43.
Keywords: Genetic algorithms; Maximum likelihood estimation; Radar
imaging; Remote sensing by radar; Simulated annealing; Synthetic aperture
radar; Genetic-annealing parameter estimation; Intensity SAR data; Maximum
likelihood estimator; Distributional parameters; Optimisation problem;
Objective function; Numerical instabilities; Stochastic optimisation ;
Urban areas
Original abstract: Finding the maximum likelihood estimators for some
distributional parameters of intensity data in synthetic aperture radar
(SAR) images is a very difficult optimisation problem due to, among other
reason, the presence of several local maxima in the objective function,
the analytical intractability of the expressions involved and numerical
instabilities. A possible approach to this problem is the use of stochastic
optimisation techniques, such as simulated annealing and genetic algorithms,
that do not get trapped into local maxima hills and, thus, make it possible
to deal with very general distributions. This work shows the results of
such approach in real situations, with images obtained from urban areas.
Doucelte, P.; P. Agouris; M. Musavi; A. Stefanidis, (1999). Automated
extraction of linear features from aerial imagery using Kohonen learning
and GIS data. Integrated Spatial Databases. Digital Images and GIS.
International Workshop ISD'99. Selected Papers (Lecture Notes in Computer
Science Vol.1737) Portland, ME, USA 14-16 June 1999
Berlin, Germany Springer-Verlag, pp.20-33.
Keywords: Feature extraction; Geographic information systems; Image
classification; Image resolution; Network topology; Remote sensing; Self-organising
feature maps; Unsupervised learning; Semi-automated linear feature extraction;
Aerial imagery; Kohonen learning; Geographic information system; Coarse-resolution
GIS vector data; Kohonen self-organizing map algorithm; Neural network;
Competitive unsupervised learning; Radiometrically classified image pixels;
Network topology initialization; Spatial structures; Network weight initialization;
Synaptic weight vector updating; Winning neural units; 2D vector shape
vertices; High-resolution hyperspectral imagery ; Center-line information
extraction
Original abstract: An approach to semi-automated linear feature extraction
from aerial imagery is introduced in which Kohonen's (1982) self-organizing
map (SOM) algorithm is integrated with existing GIS data. The SOM belongs
to a distinct class of neural networks which is characterized by competitive
and unsupervised learning. Using radiometrically classified image pixels
as input, appropriate SOM network topologies are modeled to extract underlying
spatial structures contained in the input patterns. Coarse-resolution GIS
vector data is used for network weight and topology initialization when
extracting specific feature components. The Kohonen learning rule updates
the synaptic weight vectors of winning neural units that represent 2D vector
shape vertices. Experiments with high-resolution hyperspectral imagery
demonstrate a robust ability to extract center-line information when presented
with coarse input.
Dousset, B., (1995). Synthetic aperture radar imaging of urban surfaces:
a case study. 1995 International Geoscience and Remote Sensing Symposium,
IGARSS '95. Quantitative Remote Sensing for Science and Applications (Cat.
No.95CH35770) Firenze, Italy 10-14 July 1995
New York, NY, USA IEEE, pp.2092-6 vol.3.
Keywords: Backscatter; Geophysical techniques; Hydrological techniques;
Moisture measurement; Radar cross-sections; Radar imaging; Remote sensing
by radar; Soil; Synthetic aperture radar; Geophysical measurement technique;
Radar remote sensing; Hydrology; Land surface; Terrain mapping; Soil moisture;
Water content; Urban area; California; United States; usa; SAR imaging;
Urban surface; SAR image; Los Angeles basin; Backscatter intensity; Radar
scattering; Smooth pavement; Asphalt; Road; Commercial area ; Azimuth dependence
Original abstract: A set of SAR images of the Los Angeles basin was
analyzed to assess their potential to derive soil moisture, an important
component of the surface energy balance in urban areas. Large variations
of backscatter intensities were observed for different land uses and different
SAR images. Low intensities were found over smooth pavement and asphalt.
High intensities were found over industrial and commercial areas, with
a marked illumination azimuth dependence for the latter; and maximum intensities
were found when the flight direction was parallel to building alignments.
A similar anisotropy occurred over residential areas, albeit at lower intensities.
These contaminations mask the backscatter variations resulting from other
processes, and may limit the estimation of soil moisture to undeveloped
blocks and parks, unless a correction is applied.
Dow, J. W.; R. H. Stokes, (1995). AM/FM/GIS database population using
integrated airborne remote sensing techniques. Proceedings AM/FM International.
Conference XVIII AM/FM International Conference XVIII Baltimore, MD, USA
20-23 March 1995
Aurora, CO, USA AM & FM Int, pp.297-308.
Keywords: Altimeters; Costing; Geographic information systems; Global
Positioning System; Remote sensing; Town and country planning; Visual databases;
am/fm/gis; Visual database; Airborne remote sensing; Mapping; Facilities
data; Decision processes; Cost; Data collection; Map data; Integrated sensor;
Automated mapping facilities management; Steerable scanning laser altimeter;
Differential Global Positioning System; Three-dimensional vector map data
; 3D vector map data
Original abstract: The AM/FM/GIS systems currently being employed by
industry provide for the storage of mapping and facilities data which can
be rapidly accessed and analyzed in support of decision processes. Currently
this data is obtained using methods that often result in inaccuracies and
increased cost. In addition, the data must constantly be updated to reflect
changes in the physical environment. Therefore, a need has arisen to convert
to an automated data collection procedure whereby accurate map data can
be collected in a cost effective, timely, and reoccurring manner. A current
method for conversion employs an airborne platform populated with an integrated
sensor package specifically designed to obtain and record imagery and three-dimensional
(i.e., height and location) map data. The types of imagery obtainable extend
from the visible through the infrared range. The mapping system is a steerable,
scanning laser altimeter coupled with a differential GPS to provide accurate
three-dimensional vector map data. This data is the digital elevation and
location of items scanned and is directly loadable into AM/FM/GIS systems
for immediate database population and maintenance.
Dowman, I.; A. Holmes; V. Vohra, (1995). Developments in automated
object-image registration. Integrating Photogrammetric Techniques with
Scene Analysis and Machine Vision II Orlando, FL, USA 19-21 April 1995
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.85-92.
Keywords: Feature extraction; Forestry; Image registration; Photogrammetry;
Remote sensing; Automated object-image registration; Knowledge based approach;
Features identification; Polygons; Distinctive shape; Raster data; Forest
areas; Image to map registration ; High resolution satellite imagery
Original abstract: There is considerable activity at present in feature
extraction using a knowledge based approach. Much of this work is concerned
with identifying the features. In the work described in this paper the
main interest is in extracting features which can be matched on an image
and a map. The emphasis is on polygons which have a distinctive shape and
can be extracted from vector as well as raster data. An algorithm has been
developed and tested which automatically matches forest areas on a map
with forest areas on and image in two dimensions. This algorithm is being
developed for more general use so that the problem of image to map registration
can be automated in a general way. Results are given on the use of the
algorithm to identify changes in forestry from Landsat Thematic Mapper
data and developments which can match buildings on high resolution satellite
imagery are reported. An overall strategy for full automation is presented
and discussed and future developments considered.
Drake, N. A.; S. Mackin; J. J. Settle (1999). Mapping vegetation,
soils, and geology in semiarid shrublands using spectral matching and mixture
modeling of SWIR AVIRIS imagery. Remote Sensing of Environment,
V68, (N1): 12-25.
Keywords: AVIRIS , spectral matching, linear mixture modeling techniques
Original Abstract: Spectral matching and linear mixture modeling techniques
have been applied to synthetic imagery and AVIRIS SWIR imagery of a semiarid
rangeland in order to determine their effectiveness as mapping tools, the
synergism between the two methods, and their advantages, and limitations
for rangeland resource exploitation and management. Spectral matching of
pure library spectra was found to be an effective method of locating and
identifying endmembers for mixture modeling although some problems were
found with the false identification of gypsum. Mixture modeling could accurately
estimate proportions for a large number of materials in synthetic imagery;
however, it produced high variance estimates and high error estimates when
presented with all nine AVIRIS endmembers because of high noise levels
in the imagery. The problem of which endmembers to select was addressed
by implementing a mixture model that allowed estimation of the errors on
the proportions estimates, discarding the endmembers with the highest errors,
recomputing the errors, and the proportions estimates, and iterating this
process until the mixture maps were relatively free from noise. This methodology
ensured that the lowest contrast materials were discarded. The inevitable
confusion that followed was monitored the using the maps produced by spectral
matching. Spectral matching was more effective than mixture modeling for
geological mapping because it allowed identification and mapping of the
relatively pure regions of all the surficial materials that exert an influence
on the spectral response. The maps of the different clay minerals were
of considerable value for mineral exploration purposes. Conversely, spectral
matching was less useful than mixture modeling for rangeland vegetation
studies because a classification of all pixels is needed and abundance
estimates are required for many applications. Mixture modeling allowed
identification of both nonphotosynthetic and green vegetation cover and
thus total cover. Though the green vegetation mixture map appears to be
very precise, the nonphotosynthetic vegetation estimates were poor.
Du, Q.; C. I. Chang (2001). A linear constrained distance-based discriminant
analysis for hyperspectral image classification. Pattern Recognition,
V34, (N2): 361-373.
Keywords: AI/Robotics/Automatic Control
Du, Q.; I. C. Chein; D. C. Heinz; M. L. G. Althouse; I. W. Ginsberg,
(2000). A linear mixture analysis-based compression for hyperspectral
image analysis. IGARSS 2000. IEEE 2000 International Geoscience and
Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote
Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120) Honolulu,
HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, pp.585-7 vol.2.
Keywords: Data compression; Geophysical signal processing; Geophysical
techniques; Image classification; Image coding; Multidimensional signal
processing; Remote sensing; Terrain mapping; Geophysical measurement technique;
Land surface; Linear mixture analysis; Image compression; Hyperspectral
image; Least squares linear spectral mixture analysis; Target detection;
aviris; High compression ratio; Optical image ; Multispectral remote sensing
Original abstract: The authors present a fully constrained least squares
linear spectral mixture analysis-based compression technique for hyperspectral
image analysis, particularly, target detection and classification. Unlike
most compression techniques that directly deal with image gray levels,
the proposed compression approach generates the abundance fractional images
of potential targets present in an image scene and then encodes these fractional
images so as to achieve data compression. Since the vital information used
for image analysis is generally preserved and retained in the abundance
fractional images, the loss of information may have very little impact
on image analysis. In some occasions, it even improves analysis performance.
AVIRIS data experiments demonstrate that it can effectively detect and
classify targets while achieving very high compression ratios.
Duan, L.; Y. Bao; W. Zhang (1998). GPS vehicle navigation system.
Transactions of Nanjing University of Aeronautics & Astronautics,
15, (2): 172-8.
Keywords: Automated highways; Computerised navigation; Geographic
information systems; Global Positioning System; Real-time systems; Road
traffic; GPS vehicle navigation; Intelligent vehicle navigation; Urban
fields; Inter-province; Inter-state transport; City geographical information
system; Real-time correction ; Transportation
Original Abstract: This paper introduces the GPS intelligent vehicle
navigation system (IVNS). This system can be widely applied to the urban
fields, inter-province and inter-state transport which, as a part of intelligent
transport systems (ITS), would contribute toward improving troublesome
driving conditions on driver's benefits. The paper discuss as two key techniques;
city geographical information system (GIS), real-time correction of the
deviation.
Ducksbury, P. G.; D. M. Booth; C. J. Radford, (1995). Vehicle detection
in infrared linescan imagery using belief networks. Fifth International
Conference on Image Processing and its Applications (Conf. Publ. No.410)
Fifth International Conference on Image Processing and its Applications
(Conf. Publ. No.410) Edinburgh, UK 4-6 July 1995
London, UK IEE, pp.415-19.
Keywords: Bayes methods; Feature extraction; Image classification;
Image segmentation; Infrared imaging; Object detection; Remote sensing;
Tracking; Vehicle detection; Infrared linescan imagery; Belief networks;
Airborne downward looking imagery; Pearl-Bayes network; Vehicle track detectors;
Shadow detectors; Contextual evidence; Neighbouring detections ; Feedback
loop
Original abstract: This paper describes a system for detecting vehicles
in airborne downward looking infrared linescan imagery, and in particular,
the use of a Pearl-Bayes Network (PBN) to combine disparate sources of
evidence. Here the primary source of evidence is a vehicle detection algorithm
with supporting evidence being provided by vehicle track and shadow detectors.
The spatial arrangement of the vehicles also provides useful contextual
evidence since vehicles often move in convoy or are clustered into small
groups when encamped. This observation is the basis for allowing neighbouring
detections to re-enforce one another and for incorporating a feedback loop
with which to increase the sensitivity of the vehicle detection algorithm
within areas of suspected activity.
Dueker, K. J.; J. A. Butler (2000). A geographic information system
framework for transportation data sharing. Transportation Research
Part C (Emerging Technologies), 8C, (1-6): 13-36.
Keywords: Data models; Geographic information systems; Traffic information
systems; Transaction processing; Transportation; Geographic information
system framework; Transportation data sharing; Data producers; Data integrators;
Data users; Enterprise geographic information systems-transportation; GIS-T
data model; Transportation data elements; Transportation features; Common
data model; Graphical representations; Point events; Linear referencing;
Relevant transportation features; Application-specific databases; Transactional
update system; Feature-oriented enterprise GIS-T database ; Application-specific
network databases
Original Abstract: The paper develops a framework and principles for
sharing transportation data. The framework is intended to clarify roles
among participants, data producers, data integrators, and data users. The
principles are intended to provide guidance for the participants. Both
the framework and the principles are based on an enterprise geographic
information systems-transportation (GIS-T) data model that defines relations
among transportation data elements. The data model guards against ambiguities
and provides a basis for the development of the framework and principles
for sharing transportation data. There are two central principles. First
is the uncoupling of graphics, topology, position, and characteristics,
Second is the establishment of a schema for transportation features and
their identifiers. An underlying principle is the need for a common data
model that holds transportation features, not their graphical representations,
as the objects of interest. Attributes of transportation features are represented
as linear and point events. These are located along the feature using linear
referencing. Sharing of transportation data involves exchange of relevant
transportation features and events, not links and nodes of application-specific
databases. Strategies for sharing transportation features follow from this
approach. The key strategy is to identify features in the database to facilitate
a transactional update system, one that does not require rebuilding the
entire database anew. This feature-oriented enterprise GIS-T database becomes
the basis for building separate application-specific network databases.
Dueker, K. J.; J. A. Butler (1998). GIS-T enterprise data model with
suggested implementation choices. URISA Journal, 10,
(1): 12-36.
Keywords: Cartography; Data models; Geographic information systems;
Transportation; Travel industry; Visual databases; gis-t; Enterprise data
model; Digital road map databases; Organizations; User requirements; Location
referencing systems; Interoperability ; Geometry
Original Abstract: Sharing of digital road map databases within and
among organizations is dependent on translating user requirements to a
data model that supports linear and non-linear location referencing systems.
This paper examines issues of creating such a data model with the intent
of sharing digital road map databases, and suggests implementation choices
that can accommodate a range of applications. The proposal is best characterized
as a GIS-T enterprise data model suitable for organizations responsible
for any and all modes of transportation; e.g., aviation, highways, public
transit, and railways. The proposed data model may be sufficiently robust
to support ITS map database interoperability by maintaining independence
among the geographic datum, the events that occur on the transportation
system, the geometry to represent the system cartographically, and the
paths through the system. Sample physical database designs are provided
to show how the model might be implemented.
Duff, K.; M. Hyzak
TFHRC
Structural Monitoring with GPS
Turner-Fairbank Highway Research Center (TFHRC)
http://www.tfhrc.gov/pubrds/spring97/gps.htm
Keywords: structural monitoring, GPS
Duggin, M. J.; J. A. North; E. Bohling; T. Birdsall; S. Bisgrove, (1997).
Contrast enhancement in natural scenes using multiband polarization
methods. Polarization: Measurement, Analysis, and Remote Sensing San
Diego, CA, USA 30 July-1 Aug. 1997
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.288-95.
Keywords: CCD image sensors; Feature extraction; Focal planes; Geophysical
signal processing; Image enhancement; Natural scenes; Object recognition;
Polarimetry; Remote sensing; Contrast enhancement; Multiband polarization
methods; Digital imaging cameras; Linear drivers; Multiband imaging polarimeter;
Digital image acquisition; Quantitative method; Target discrimination;
Feature recognition; Shadow penetration; Terrestrial images; Digital photography;
ccd fpa ; Polarimetric mapping
Original abstract: Relatively little work has been performed to investigate
the potential of polarization techniques to provide contrast enhancement
in natural scenes. Largely, this is because film is less accurate radiometrically
than digital CCD FPA sensing devices. Such enhancement is additional to
that provided by between-band differences for multiband data. Recently,
Kodak has developed several digital imaging cameras which were intended
for professional photographers. However, the application of linear drivers
to read the data from the camera into the computer has resulted in a device
which can be used as a multiband imaging polarimeter. Here we examine the
potential of digital image acquisition as a potential quantitative method
to obtain new information additional to that obtained by multiband or even
hyperspectral imaging methods. We present an example of an active on-going
research program.
Duncan, G.; W. Heidbreder; C. Szpak; J. Hammack; B. Morey, (1998). A
multilevel technique for image enhancement improvement using RADARSAT.
Algorithms for Multispectral and Hyperspectral Imagery IV Orlando, FL,
USA 13-14 April 1998
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.86-99.
Keywords: Feature extraction; Geophysical signal processing; Image
enhancement; Image recognition; Radar imaging; Remote sensing by radar;
Multilevel technique; Image enhancement improvement; radarsat; Image quality
improvement; Extraction; Map features; Sensor imagery; Visual metrics;
Shoreline categorization ; Delineation
Original abstract: This paper presents an image quality improvement
approach for extraction of map features based on single and multisensor
image enhancement techniques. The approach relies upon the evaluation of
sensor imagery for extraction of map features based on its sensor characteristics.
The approach is illustrated by evaluation results for RADARSAT using two
visual metrics. Samples of single sensor and multiband enhancement results
are presented for a range of map features. Features include those essential
for shoreline categorization and delineation for support of environmental
applications.
Duncan, G.; W. H. Heidbreder; J. Hammack; C. Szpak, (1997). Map feature
examination of RADARSAT for geospatial utility and imagery enhancement
opportunities. Integrating Photogrammetric Techniques with Scene Analysis
and Machine Vision III Orlando, FL, USA 21-23 April 1997
SPIE-Int. Soc. Opt. Eng
Proc. SPIE - Int. Soc. Opt. Eng. (USA), pp.55-66.
Keywords: Cartography; Feature extraction; Geography; Geophysical
signal processing; Image enhancement; Radar imaging; Remote sensing by
radar; Map feature examination; radarsat; Geospatial utility enhancement;
Geospatial imagery enhancement; Multisensor tests; Multisensor enhancement
techniques; Data collection; Data analysis ; Product-source prediction
capability
Original abstract: This paper discusses multisensor tests conducted
to examine the geospatial information potential of RADARSAT imagery. The
focus of the tests is to develop a metric for determining which map features
present in optical or radar imagery could benefit from the use of multisensor
enhancement techniques. Visual and geospatial differences between optical
and radar imagery are studied. Data collection and analysis are based on
a product-source prediction capability (PSPC) and other related modeling
and analysis tools.
Duskunovic, I.; G. Heene; W. Philips; I. Bruyland, (2000). Urban
area detection in SAR imagery using a new speckle reduction technique and
Markov random field texture classification. IGARSS 2000. IEEE 2000
International Geoscience and Remote Sensing Symposium. Taking the Pulse
of the Planet: The Role of Remote Sensing in Managing the Environment.
Proceedings (Cat. No.00CH37120) Honolulu, HI, USA 24-28 July 2000
Piscataway, NJ, USA IEEE, v.2, pp.636-8.
Keywords: SAR imagery Urban area detection, Speckle reduction, Markov
random field,Texture classification
Synopsis: The authors introduce a speckle reduction technique to reduce
the effects of scatter interference in SAR data. They then use the Markov
Random Field texture classification method for urban dectection. Results
show that classification is more accurate when their speckle reduction
technique is used as compared to traditional speckle filters.
Original abstract: The strength of synthetic aperture radar (SAR) as
a land observation tool resides in S. Gautama et al. (1998) the sensitivity
of radar backscatter to the moisture content of terrain media and to the
geometrical parameters of the scatterers in the media (i.e., size, shape,
roughness and orientation), and (2) an all-weather, day or night imaging
capability. However, SAR images are degraded by multiplicative speckle
noise due to interference between individual scatterers within one resolution
cell. The authors first propose a new speckle reduction method, which preserves
edges and doesn't need parameters to be adjusted, based on wavelet decomposition.
Comparison with standard speckle filters shows that their filter removes
speckle better, while preserving the same amount of detail. Next they use
the filter technique in combination with the Markov random field (MRF)
texture classification as stated in by S. Gautama et al. (1998) to detect
urban areas in the images. The results show that classification results
are better when using their proposed filter, compared to the classification
results with images filtered with standard speckle filters.
Dutra, L. V.; R. Huber; P. Hernandez, (1998). Primary forest and
land cover contextual classification using JERS-1 data in Amazonia, Brazil.
IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International
Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174)
Seattle, WA, USA 6-10 July 1998
New York, NY, USA IEEE, pp.2743-5 vol.5.
Keywords: Feature extraction; Forestry; Geophysical signal processing;
Geophysical techniques; Geophysics computing; Image classification; Multilayer
perceptrons; Radar imaging; Remote sensing by radar; Spaceborne radar;
Synthetic aperture radar; Terrain mapping; Vegetation mapping; Geophysical
measurement technique; Land surface; Forest; Radar remote sensing; Land
cover contextual classification; sar; L-band; jers-1; Amazonia; Amazon;
Brazil; Image texture; Image feature; Neural net; Neural network; Multilayer
perceptron; Contextual method; Tapajos National Forest ; Para State
Original abstract: The authors present a methodology for land cover
and primary forest mapping in Amazonia using textural features derived
from JERS-1 data and classified with a multilayer perceptron based contextual
method. Land cover classification is an important step towards the use
of radar data as a tool for land use change studies in Amazonia. Also,
primary forest classification is an important issue in ecosystem studies
and economical assessment of sustainable timber exploitation. The use of
radar data, particularly L-band data, is justifiable as large Amazonian
area is permanently cloud covered. Considering a set of primary forest
and land use classes of interest in the Tapajos National Forest and adjacent
regions, Para State, Brazil, it was investigated which classes could be
distinguished using textural features derived by co-occurrence and matched
filtering techniques. Nondiscriminating classes were grouped together to
form new classes resulting in two classes of primary forest, three classes
of land use, water and aquatic vegetation. The feature set with higher
overall accuracy was used to classify a small mosaic of the region, using
a contextual neural network based classifier with 87% overall accuracy.