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Proceedings Paper

Efficient regularized LDA for hyperspectral image classification
Author(s): Tatyana V. Bandos; Lorenzo Bruzzone; Gustavo Camps-Valls
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Paper Abstract

In this paper, we focus on different kinds of regularization for Linear Discriminant Analysis (LDA) in the context of ill-posed remote sensing image classification problems. Several LDA-based classifiers are studied theoretically and tested on various remote sensing datasets. In addition, we introduce an efficient version of the standard regularized LDA recently presented in Ref. 1 to cope with high-dimensional small sample size (ill-posed) problems. Experimental results demonstrate the suitability of the proposal.

Paper Details

Date Published: 24 October 2007
PDF: 12 pages
Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480R (24 October 2007); doi: 10.1117/12.737157
Show Author Affiliations
Tatyana V. Bandos, Univ. de València (Spain)
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)
Gustavo Camps-Valls, Univ. de València (Spain)


Published in SPIE Proceedings Vol. 6748:
Image and Signal Processing for Remote Sensing XIII
Lorenzo Bruzzone, Editor(s)

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