Discriminative graphical models for sparsity-based hyperspectral target detection
The inherent discriminative capability of sparse representations has been exploited recently for hyperspectral target detection. This approach relies on the observation that the spectral signature of a pixel can be represented as a linear combination of a few training spectra drawn from both target and background classes. The sparse representation corresponding to a given test spectrum captures class-specific discriminative information crucial for detection tasks. Spatio-spectral information has also been introduced into this framework via a joint sparsity model that simultaneously solves for the sparse features for a group of spatially local pixels, since such pixels are highly likely to have similar spectral characteristics. In this paper, we propose a probabilistic graphical model framework that can explicitly learn the class conditional correlations between these distinct sparse representations corresponding to different pixels in a spatial neighborhood. Simulation results show that the proposed algorithm outperforms classical hyperspectral target detection algorithms as well as support vector machines.