Efficient regularized LDA for hyperspectral image classification

Tatyana V. Bandos; Lorenzo Bruzzone; Gustavo Camps-Valls

doi:10.1117/12.737157

24 October 2007 Efficient regularized LDA for hyperspectral image classification

Tatyana V. Bandos, Lorenzo Bruzzone, Gustavo Camps-Valls

Proceedings Volume 6748, Image and Signal Processing for Remote Sensing XIII; 67480R (2007) https://doi.org/10.1117/12.737157
Event: SPIE Remote Sensing, 2007, Florence, Italy

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.

Citation Download Citation

Tatyana V. Bandos, Lorenzo Bruzzone, and Gustavo Camps-Valls "Efficient regularized LDA for hyperspectral image classification", Proc. SPIE 6748, Image and Signal Processing for Remote Sensing XIII, 67480R (24 October 2007); https://doi.org/10.1117/12.737157

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