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Proceedings Paper

Classification of hyperspectral images with support vector machines: multiclass strategies
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Paper Abstract

This paper addresses the problem of the classification of hyperspectral remote-sensing images by means of Support Vector Machines (SVMs). In a first step, we propose a theoretical and experimental analysis that aims at assessing the properties of SVM classifiers in hyperdimensional feature spaces which are compared with those of other nonparametric classifiers. In a second step, we face the multiclass problem involved by SVM classifiers when applied to hyperspectral data. In particular, four different multiclass strategies are analyzed and compared: the one-against-all, the one-against-one and two hierarchical tree-based strategies. The experimental analysis has been carried out by using hyperspectral images acquired by the AVIRIS sensor on the Indian Pine area. Different performance indicators have been used to support our experimental studies, i.e., the classification accuracy, the computational time, the stability to parameter setting, and the complexity of the multiclass architecture adopted. The obtained results confirm the effectiveness of SVMs in hyperspectral data classification with respect to conventional classifiers.

Paper Details

Date Published: 5 February 2004
PDF: 12 pages
Proc. SPIE 5238, Image and Signal Processing for Remote Sensing IX, (5 February 2004); doi: 10.1117/12.514275
Show Author Affiliations
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)
Farid Melgani, Univ. degli Studi di Trento (Italy)

Published in SPIE Proceedings Vol. 5238:
Image and Signal Processing for Remote Sensing IX
Lorenzo Bruzzone, Editor(s)

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