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Proceedings Paper

Support vector machines in remote sensing: the tricks of the trade
Author(s): Gustavo Camps-Valls
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Paper Abstract

Support Vector Machines (SVM) have been widely adopted by the remote sensing community in the last decade. The standard algorithm has been mainly applied to image classication tasks. Many advanced developments based on SVM have been introduced as well. This paper, nevertheless, revises the standard formulation of SVM. An important part of the paper is about the intuition on the SVM parts: the cost, the regularizer and the free parameters. Finally, the paper revises three interesting simple modications well suited to tackle remote sensing image classication: constraining the margin, including invariances and the information of unlabeled samples. Some examples are given to illustrate these concepts.

Paper Details

Date Published: 27 October 2011
PDF: 9 pages
Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800B (27 October 2011); doi: 10.1117/12.903949
Show Author Affiliations
Gustavo Camps-Valls, Univ. de València (Spain)


Published in SPIE Proceedings Vol. 8180:
Image and Signal Processing for Remote Sensing XVII
Lorenzo Bruzzone, Editor(s)

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