Efficient regularized LDA for hyperspectral image classification

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 dataseis. 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.