Sparse-Based Classification of Hyperspectral Images Using Extended Hidden Markov Random Fields

Abstract

This paper proposes a spectral-spatial method for the classification of hyperspectral images (HSIs). The proposed method consists of suggesting two main techniques. First, to construct dictionaries and sparse codes containing the least spatial-spectral correlation, an objective function for a sparse classifier, namely optimum dictionary learning (ODL), is proposed. This objective function combines the spectral and spatial features in a hierarchical fashion. ODL is applied in the nonsubsampled pyramid domain to exploit the multiresolution property of pyramids. Second, to enhance the classification accuracy, an extended hidden Markov random field (EHMRF) is suggested. The EHMRF uses the Poisson distribution and weighted mean regularization to overcome Poisson noise and to increase the local neighborhood energy consistency, respectively. Finally, in order to estimate the parameter of the Poisson distribution, an iterative maximum likelihood (ML) algorithm is used. In comparison with some of state-of-the-art hidden Markov random field and sparse-based classifier methods, the experimental results of the proposed method (ODL-EHMRF-ML) show that it significantly increases the classification accuracy of HSIs.