Table Of ContentMachine Vision and
Advanced Image Processing
in Remote Sensing
Springer
Berlin
Heidelberg
New York
Barcelona
Hong Kong
London
Milan
Paris
Singapore
Tokyo
I. Kanellopoulos . G. G. Wilkinson
T. Moons (Eds.)
Machine Vision and
Advanced Image Processing
in Remote Sensing
Proceedings of Concerted Action MAVIRIC
(Machine Vision in Remotely Sensed Image
Comprehension)
With 165 Figures
and 13 Tables
, Springer
Ioannis KaneUopouios
Joint Research Centre. Commission of the European Communities
Space Applications Institute. Environment and Geo-Information Unit
1-21020 Ispra (Varese)
Italy
Prof. Graeme G. Wilkinson
Kingston University
School of Computer Science and Electronic Systems
Penrhyn Road
Kingston Upon Thames, KT1 2EE
United Kingdom
Dr. Theo Moons
Katbolieke Universiteit Leuven
Department of Electrotechnical Engineering (ESAT)
Centre for Processing Speech and Images (PSI)
Kardinaal Mercierlaan 94
B·3001 Heverlee
Belgium
ISBN·13: 978·3·642·64260·9 e·ISBN·13: 978·3·642·60105·7
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M"hine vision and advanced image processing in remote sensing: proceedings of coneened "tion
MAVIRIC (machine vilion in remotely sensed. image compteheruion); with 13 tables f I. KaneUo
poulos ... (td.). - Berlin; Heidelberg; New York; Barcelona; Hong Kong; London; Milan; Paris;
Singapore; Tokyo: Springer, 1m
ISBN·13: 978-3-M24260·9
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Foreword
Since 1994, the European Commission has undertaken various actions to
expand the use of Earth observation (EO) from space in the Union and to
stimulate value-added services based on the use of Earth observation satellite
data.' By supporting research and technological development activities in this
area, DG XII responded to the need to increase the cost-effectiveness of space
derived environmental information. At the same time, it has contributed to
a better exploitation of this unique technology, which is a key source of data
for environmental monitoring from local to global scale.
MAVIRIC is part of the investment made in the context of the Environ
ment and Climate Programme (1994-1998) to strengthen applied techniques,
based on a better understanding of the link between the remote sensing signal
and the underlying bio- geo-physical processes. Translation of this scientific
know-how into practical algorithms or methods is a priority in order to con
vert more quickly, effectively and accurately space signals into geographical
information. Now the availability of high spatial resolution satellite data is
rapidly evolving and the fusion of data from different sensors including radar
sensors is progressing well, the question arises whether existing machine vision
approaches could be advantageously used by the remote sensing community.
Automatic feature/object extraction from remotely sensed images looks very
attractive in terms of processing time, standardisation and implementation
of operational processing chains, but it remains highly complex when applied
to natural scenes.
The MAVIRIC concerted action provides an international forum gathering
numerous acknowledged European experts from both the machine vision and
the remote sensing communities. Recent assessment of the state-of-the-art on
the application of machine vision or visual processing techniques in remote
sensing has been made during a highly successful workshop held in May 1998.
The present volume summarises the outcome of this workshop, and draws
up research priorities in this area that could lead to new remote sensing
applications and market segments. I believe it represents a very significant
contribution to this field.
Michel Schouppe
Environment & Climate Programme
DG XII: Science, research and development
European Commission
Table of Contents
Foreword..................................................... v
Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Part I. Image Processing and Computer Vision Methods for
Remote Sensing Data
Recent Developments in Remote Sensing Technology and
the Importance of Computer Vision Analysis Techniques
Graeme G. Wilkinson ........................................... 5
Posing Structural Matching in Remote Sensing as an
Optimisation Problem
Peter Forte and Graeme A. Jones. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 12
Detail-Preserving Processing of Remote Sensing Images
Silvana Dellepiane .............................................. 23
Multi-Channel Remote Sensing Data and Orthogonal
Transformations for Change Detection
Allan Aasbjerg Nielsen .......................................... 37
Aspects of Multi-Scale Analysis for Managing Spectral and
Temporal Coverages of Space-Borne High-Resolution Images
Bruno Aiazzi, Luciano Alparone, Alessandro Barducci, Stefano Baronti,
Roberto Carla, and Ivan Pippi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 49
Structural Inference Using Deformable Models
Jens Michael Carstensen, Rune Fisker, ~ette Schultz, and Torsten Dorge 61
Terrain Feature Recognition Through Structural Pattern
Recognition, Knowledge-Based Systems, and
Geomorphometric Techniques
Dimitris P. Argialas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 72
viii Table of Contents
Part II. High Resolution Data
Environmental Mapping Based on High Resolution Remote
Sensing Data
Kolbeinn Amason and Jon Atli Benediktsson. . . . . . . . . . . . . . . . . . . . . .. 89
Potential Role of Very High Resolution Optical Satellite
Image Pre-Processing for Product Extraction.
P. Boekaerts, V. Christopoulos, A. Munteanu, and J. Comelis ........ 100
Forestry Applications of High Resolution Imagery
Thomas Harne, Mikael Holm, Susanna Rautakorpi, and Eija Parmes ... 111
Image Analysis Techniques for Urban Land Use
Classification. The Use of Kernel Based Approaches to
Process Very High Resolution Satellite Imagery
Charalambos C. Kontoes ......................................... 121
Part III. Visualisation, 3D and Stereo
Automated Change Detection in Remotely Sensed Imagery
Joseph Mundy and Rupert Curwen ................................ 137
A 3-Dimensional Multi-View Based Strategy for Remotely
Sensed Image Interpretation
Theo Moons, David Frere, and Luc Van Gool ....................... 148
3D Exploitation of SAR Images
Regine Bolter and Axel Pinz ..................................... 160
Visualizing Remotely Sensed Depth Maps using Voxels
Nilo Stolte ..................................................... 170
Three Dimensional Surface Registration of Stereo Images
and Models from MR Images
HenningNielsen, Lasse Riis 0stergaard, and David Le Gall .......... 181
Exploring Multi-Dimensional Remote Sensing Data with a
Virtual Reality System
Walter Di Carlo ................................................ 189
Table of Contents IX
Part IV. Image Interpretation and Classification
Information Mining in Remote Sensing Image Archives
Mihai Datcu, Klaus Seidel, and Gottfried Schwarz ................... 199
Fusion of Spatial and Temporal Information for Agricultural
Land Use Identification - Preliminary Study for the
VEGETATION Sensor
Jean-Paul Berroir ............................................... 213
Rule-based Identification of Revision Objects in Satellite
Images
Camilla Mahlander and Dan Rosenholm ........................... 219
Land Cover Mapping from Optical Satellite Images
Employing Subpixel Segmentation and Radiometric
Calibration
Werner Schneider ............................................... 229
Semi-Automatic Analysis of High-Resolution Satellite
Images
Wim Mees and Marc Acheroy .................................... 238
Density-Based Unsupervised Classification for Remote
Sensing
Cees H. M. van Kemenade, Han La Poutre, and Robert J. Mokken .... 248
Classification of Compressed Multispectral Data
Frank Tintrup, Cristina Perra, and Gianni Vernazza ................. 259
Part V. Segmentation and Feature Extraction
Detection of Urban Features Using Morphological Based
Segmentation and Very High Resolution Remotely Sensed
Data
Martino Pesaresi and Ioannis Kanellopoulos ........................ 271
Non-Linear Line Detection Filters
Isabelle Gracia and Maria Petrou ................................. 285
Fuzzy Clustering and Pyramidal Hough Transform for
Urban Features Detection in High Resolution SAR Images
Eugenio Costamagna, Paolo Gamba, Giacomo Sacchi, and Pietro Savazzi295
x Table of Contents
Detecting Nets of Linear Structures in Satellite Images
Petia Radeva, Andres Sole, Antonio M. Lopez, and Joan Serrat ....... 304
Satellite Image Segmentation Through Rotational Invariant
Feature Eigenvector Projection
Albert Pujol, Andres Sole, Daniel Ponsa, Javier Varona, and Juan Jose
Villanueva ..................................................... 317
Supervised Segmentation by Region Merging
Ben Gorte ..................................................... 328
Part I
Image Processing and Computer Vision
Methods for Remote Sensing Data
Description:Since 1994, the European Commission has undertaken various actions to expand the use of Earth observation (EO) from space in the Union and to stimulate value-added services based on the use of Earth observation satellite data.' By supporting research and technological development activities in this
