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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)

Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension) by Ioannis Kanellopoulos,Graeme G. Wilkinson,Theo Moons

ISBN10: 3540655719
ISBN13: 978-3540655718
Author: Ioannis Kanellopoulos,Graeme G. Wilkinson,Theo Moons
Book title: Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)
Publisher: Springer (June 22, 1999)
Language: English
Category: Computer Science
Size PDF: 1716 kb
Size ePub: 1376 kb
Size Fb2: 1955 kb
Rating: 4.9/5
Votes: 178
Pages: 335 pages

Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension) by Ioannis Kanellopoulos,Graeme G. Wilkinson,Theo Moons



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.

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