A sparsity-based Bayesian approach for hyperspectral unmixing using normal compositional model

A new Bayesian-based method is developed for unmixing of hyperspectral images. Endmembers are assumed variable based on the Gaussian distribution. A semi-supervised scenario is considered, and as a practical aspect, the abundance vectors are assumed sparse. We propose the Dirichlet prior to represent the sparsity and derive the corresponding posteriors in Bayesian sense. Numerical results are used to evaluate different methods for both simulated and real data. It is shown that the proposed method achieves a lower error in abundance estimation and image reconstruction.