Sparsity promoting dimensionality reduction for classification of high dimensional hyperspectral images

Sparse representation is an active research area in the signal processing and machine learning community in recent years. Recently, sparse representation classifier was proposed for challenging classification tasks - it entails representing a testing sample as a linear combination of all training samples which form an over-complete dictionary. In this paper, we demonstrate that for challenging high-dimensional classification tasks, appropriate dimensionality reduction is beneficial for sparse representation classifiers and it´s variants - especially when some features are redundant and/or lack discriminatory power. We propose a new dimensionality reduction algorithm to optimize the performance of greedy pursuit algorithms (required in sparse representation classifiers) by projecting the data into a space where the ratio of intra-class to inter class inner products are maximized. We demonstrate the superiority of the proposed method with standard hyperspectral imagery datasets - both in terms of improved classification accuracy and a speed-up in the run-time.