Linear Feature Extraction for Hyperspectral Images Based on Information Theoretic Learning

Abstract

This letter proposes a new supervised linear feature extractor for hyperspectral image classification. The criterion for feature extraction is a modified maximal relevance and minimal redundancy (MRMD), which has been used for feature selection until now. The MRMD is a function of mutual information terms, which possess higher order statistics of data; thus, it is effective for hyperspectral data with informative higher order statistics. The batch and stochastic versions of the gradient ascent are performed on the MRMD to find the optimal parameters of a linear feature extractor. Preliminary results achieve better classification performance than the traditional methods based on the first- and second-order moments of data.