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In this paper a novel hierarchical Bayesian model for sparse semisupervised hyperspectral unmixing is presented. Adopting the sparsity hypothesis and taking into account the convex constraints of the estimation problem, suitable priors are selected for the model parameters. Then, a new low-complexity, iterative conditional expectations algorithm is developed to perform Bayesian inference. The proposed method converges fast to a sparse solution, which offers improved estimation accuracy. The theoretical results presented in the paper are fully verified by simulations both on synthetic and real hyper spectral data.
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