Kernel methods for remote sensing data analysis [electronic resource] / edited by Gustavo Camps-Valls, Lorenzo Bruzzone.
Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful a...
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| Online Access: |
Full Text (via Wiley) |
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| Other Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Chichester, U.K. :
Wiley,
2009.
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| Subjects: |
| Summary: | Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage asses. |
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| Physical Description: | 1 online resource (xxix, 403 pages, 8 unnumbered pages of plates) : illustrations (some color), maps. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9780470748992 0470748990 9780470749005 0470749008 0470722118 9780470722114 9786612291708 6612291702 |
| Language: | English. |