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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) - Tapa blanda

 
9783642642609: Machine Vision and Advanced Image Processing in Remote Sensing: Proceedings of Concerted Action MAVIRIC (Machine Vision in Remotely Sensed Image Comprehension)

Sinopsis

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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Reseña del editor

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.

Reseña del editor

This volume describes some of the latest developments in the techniques for the analysis of remotely sensed satellite imagery. Focussing in particular on structural information and image understanding, the book will provide a useful guide to emerging techniques which will be of value in the exploitation of the new generation of very high resolution imagery. A number of contributions draw on recent work in the computer vision field. The book explores the increasing links between computer vision and remote sensing, and includes chapters from authors working in the two fields. It brings together the work of a number of European experts who participated in the events of a Concerted Action supported by the Environment and Climate Programme of the European Commission.

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Ioannis Kanellopoulos
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 352 pp. Englisch. Nº de ref. del artículo: 9783642642609

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Kanellopoulos, Ioannis|Wilkinson, Graeme G.|Moons, Theo
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Good overview of the state of the art in Europe in advanced image analysis techniques for remote sensingInsight to computer vision techniques and software tools that could be used in future remote sensing projectsFocusses primarily on new (and forth. Nº de ref. del artículo: 5066504

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Ioannis Kanellopoulos
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783642642609

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Kanellopoulos, Ioannis (EDT); Wilkinson, Graeme G. (EDT); Moons, Theo (EDT)
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 352 pp. Englisch. Nº de ref. del artículo: 9783642642609

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Moons Theo Kanellopoulos Ioannis Wilkinson Graeme G.
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