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Machine Vision and Advanced Image Processing in Remote Sensing

Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)

  • Conference proceedings
  • © 1999

Overview

Editors:
  1. Ioannis Kanellopoulos

    1. Joint Research Centre, Commission of the European Communities, Space Applications Institute, Environment and Geo-Information Unit, Ispra (Varese), Italy

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  2. Graeme G. Wilkinson

    1. School of Computer Science and Electronic Systems, Kingston University, Kingston Upon Thames, UK

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  3. Theo Moons

    1. Department of Electrotechnical Engineering (ESAT) Centre for Processing Speech and Images (PSI), Katholieke Universiteit Leuven, Belgium

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  • Good overview of the state of the art in Europe in advanced image analysis techniques for remote sensing
  • Insight to computer vision techniques and software tools that could be used in future remote sensing projects
  • Focusses primarily on new (and forthcoming) very high-resolution satellite imagery

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Table of contents (30 papers)

  1. Front Matter

    Pages i-x
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  2. Image Processing and Computer Vision Methods for Remote Sensing Data

    1. Front Matter

      Pages 3-3
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    2. Recent Developments in Remote Sensing Technology and the Importance of Computer Vision Analysis Techniques

      • Graeme G. Wilkinson
      Pages 5-11

    3. Posing Structural Matching in Remote Sensing as an Optimisation Problem

      • Peter Forte, Graeme A. Jones
      Pages 12-22

    4. Detail-Preserving Processing of Remote Sensing Images

      • Silvana Dellepiane
      Pages 23-36

    5. Multi-Channel Remote Sensing Data and Orthogonal Transformations for Change Detection

      • Allan Aasbjerg Nielsen
      Pages 37-48

    6. Aspects of Multi-Scale Analysis for Managing Spectral and Temporal Coverages of Space-Borne High-Resolution Images*

      • Bruno Aiazzi, Luciano Alparone, Lessandro Barducci, Stefano Baronti, Roberto Carlà, Ivan Pippi
      Pages 49-60

    7. Structural Inference Using Deformable Models

      • Jens Michael Carstensen, Rune Fisker, Nette Schultz, Torsten Dörge
      Pages 61-71

    8. Terrain Feature Recognition Through Structural Pattern Recognition, Knowledge- Based Systems, and Geomorphometric Techniques

      • Dimitris P. Argialas
      Pages 72-85

  3. High Resolution Data

    1. Front Matter

      Pages 87-87
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    2. Environmental Mapping Based on High Resolution Remote Sensing Data*

      • Kolbeinn Arnason, Jon Atli Benediktsson
      Pages 89-99

    3. Potential Role of Very High Resolution Optical Satellite Image Pre-Processing for Product Extraction

      • P. Boekaerts, V. Christopoulos, A. Munteanu, J. Cornelis
      Pages 100-110

    4. Forestry Applications of High Resolution Imagery

      • Tuomas Häme, Mikael Holm, Susanna Rautakorpi, Eija Parmes
      Pages 111-120

    5. Image Analysis Techniques for Urban Land Use Classification. The Use of Kernel Based Approaches to Process Very High Resolution Satellite Imagery

      • Charalambos C. Kontoes
      Pages 121-133

  4. Visualisation, 3D and Stereo

    1. Front Matter

      Pages 135-135
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    2. Automated Change Detection in Remotely Sensed Imagery

      • Joseph Mundy, Rupert Curwen
      Pages 137-147

    3. A 3-Dimensional Multi-View Based Strategy for Remotely Sensed Image Interpretation*

      • Theo Moons, David Frère, Luc Van Gool
      Pages 148-159

    4. 3D Exploitation of SAR Images

      • Regine Bolter, Axel Pinz
      Pages 160-169

    5. Visualizing Remotely Sensed Depth Maps using Voxels

      • Nilo Stolte
      Pages 170-180

    6. Three Dimensional Surface Registration of Stereo Images and Models from MR Images

      • Henning Nielsen, Lasse Riis Østergaard, David Le Gall
      Pages 181-188
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Keywords

About this book

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.

Editors and Affiliations

  • Joint Research Centre, Commission of the European Communities, Space Applications Institute, Environment and Geo-Information Unit, Ispra (Varese), Italy

    Ioannis Kanellopoulos

  • School of Computer Science and Electronic Systems, Kingston University, Kingston Upon Thames, UK

    Graeme G. Wilkinson

  • Department of Electrotechnical Engineering (ESAT) Centre for Processing Speech and Images (PSI), Katholieke Universiteit Leuven, Belgium

    Theo Moons

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