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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)

  • Ioannis Kanellopoulos
  • Graeme G. Wilkinson
  • Theo Moons

Table of contents

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

  3. High Resolution Data

    1. Front Matter
      Pages 87-87
    2. Kolbeinn Arnason, Jon Atli Benediktsson
      Pages 89-99
    3. P. Boekaerts, V. Christopoulos, A. Munteanu, J. Cornelis
      Pages 100-110
    4. Tuomas Häme, Mikael Holm, Susanna Rautakorpi, Eija Parmes
      Pages 111-120
  4. Visualisation, 3D and Stereo

    1. Front Matter
      Pages 135-135
    2. Joseph Mundy, Rupert Curwen
      Pages 137-147
    3. Regine Bolter, Axel Pinz
      Pages 160-169
    4. Henning Nielsen, Lasse Riis Østergaard, David Le Gall
      Pages 181-188
  5. Image Interpretation and Classification

    1. Front Matter
      Pages 197-197
    2. Mihai Datcu, Klaus Seidel, Gottfried Schwarz
      Pages 199-212
    3. Camilla Mahlander, Dan Rosenholm
      Pages 219-228
    4. Cees H. M. van Kemenade, Han La Poutre, Robert J. Mokken
      Pages 248-258
    5. Frank Tintrup, Cristina Perra, Gianni Vernazza
      Pages 259-267
  6. Segmentation and Feature Extraction

    1. Front Matter
      Pages 269-269
    2. Isabelle Gratia, Maria Petrou
      Pages 285-294
    3. Eugenio Costamagna, Paolo Gamba, Giacomo Sacchi, Pietro Savazzi
      Pages 295-303
    4. Petia Radeva, Andres Solé, Antonio M. López, Joan Serrat
      Pages 304-316
    5. Albert Pujol, Andrés Solé, Daniel Ponsa, Javier Varona, Juan José Villanueva
      Pages 317-327

About these proceedings

Introduction

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.

Keywords

3D Augmented Reality Change detection Computer Vision Hough transform Image segmentation LED Stereo filter image analysis image processing machine vision remote sensing visualization

Editors and affiliations

  • Ioannis Kanellopoulos
    • 1
  • Graeme G. Wilkinson
    • 2
  • Theo Moons
    • 3
  1. 1.Joint Research Centre, Commission of the European CommunitiesSpace Applications Institute, Environment and Geo-Information UnitIspra (Varese)Italy
  2. 2.School of Computer Science and Electronic SystemsKingston UniversityKingston Upon ThamesUK
  3. 3.Department of Electrotechnical Engineering (ESAT) Centre for Processing Speech and Images (PSI)Katholieke Universiteit LeuvenBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-60105-7
  • Copyright Information Springer-Verlag Berlin Heidelberg 1999
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-64260-9
  • Online ISBN 978-3-642-60105-7
  • Buy this book on publisher's site