Improving Hyperspectral Image Classification Using Spectral Information Divergence

Abstract

In order to improve the classification performance for hyperspectral image (HSI), a sparse representation classifier based on spectral information divergence (SID) is proposed. SID measures the discrepancy of probabilistic behaviors between the spectral signatures of two pixels from the aspect of information theory, which can be more effective in preserving spectral properties. Thus, the new method measures the similarity between the reconstructed pixel and the true pixel by SID instead of by the L2 norm used in traditional sparse model. Moreover, the spatial coherency across neighboring pixels sharing a common sparsity pattern is taken into account during the construction of SID-based joint sparse representation model. We propose a new version of the orthogonal matching pursuit method to solve SID-based recovery problems. The proposed SID-based algorithms are applied to real HSI for classification. Experimental results show that our algorithms outperform the classical sparse representation based classification algorithms in most cases.