Dimension reduction of hyperspectral images with sparse linear discriminant analysis

Abstract

Hyperspectral imagery generally contains enormous amounts of data due to hundreds of spectral bands. Classification for these high-dimensional data often requires a large set of training samples and enormous processing time. Therefore, dimension reduction methods for hyperspectral data are catching the attention of researchers lately. In this paper, a dimension reduction method based on sparse penalty regularized linear discriminant analysis was experimented on hyperspectral data. Through imposing sparsity regularization penalty on the Fisher's discriminant analysis projection matrix via the optimal scoring technique, sparse linear discriminant vectors can be achieved. Therefore, interpretability of the spectral bands' physical meaning and effective low dimensional data transforming can be achieved simultaneously in the same model. Experimental analysis on the sparsity and efficacy of low dimensional outputs showed that, sparse linear discriminant analysis can yield good classification results and interpretability in spectral domain.