Feature space discriminant analysis for hyperspectral data feature reduction

Abstract

Hyperspectral images contain a large number of spectral bands that allows us to distinguish different classes with more details. But, the number of available training samples is limited. Thus, feature reduction is an important step before classification of high dimensional data. Supervised feature extraction methods such as LDA, GDA, NWFE, and MMLDA use two criteria for feature reduction: between-class scatter and within-class scatter. We propose a supervised feature extraction method in this paper that uses a new criterion in addition to two mentioned measures. The proposed method, which is called feature space discriminant analysis (FSDA), at first, maximizes the between-spectral scatter matrix to increase the difference between extracted features. In the second step, FSDA, maximizes the between-class scatter matrix and minimizes the within-class scatter matrix simultaneously. The experimental results on five popular hyperspectral images show the better performance of FSDA in comparison with other supervised feature extraction methods in small sample size situation.