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Proceedings Paper

Support vector machines for remote sensing image classification
Author(s): Fabio Roli; Giorgio Fumera
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Paper Abstract

In the last decade, the application of statistical and neural network classifiers to remote-sensing images has been deeply investigated. Therefore, performances, characteristics, and pros and cons of such classifiers are quite well known, even from remote-sensing practitioners. In this paper, we present the application to remote-sensing image classification of a new pattern recognition technique recently introduced within the framework of the Statistical Learning Theory developed by V. Vapnik and his co-workers, namely, the Support Vector Machines (SVMs). In section 1, the main theoretical foundations of SVMs are presented. In section 2, experiments carried out on a data set of multisensor remote-sensing images are described, with particular emphasis on the design and training phase of a SVM. In section 3, the experimental results are reported, together with a comparison between the performances of SVMs, neural network, and k-NN classifiers.

Paper Details

Date Published: 19 January 2001
PDF: 7 pages
Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); doi: 10.1117/12.413892
Show Author Affiliations
Fabio Roli, Univ. of Cagliari (Italy)
Giorgio Fumera, Univ. of Cagliari (Italy)

Published in SPIE Proceedings Vol. 4170:
Image and Signal Processing for Remote Sensing VI
Sebastiano Bruno Serpico, Editor(s)

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