Robust spectral unmixing for anomaly detection

This paper is concerned with a joint Bayesian formulation for determining the endmembers and abundances of hyperspectral images along with sparse outliers which can lead to estimation errors unless accounted for. We present an inference method that generalizes previous work and provides a MCMC estimate of the posterior distribution. The proposed method is compared empirically to state-of-the-art algorithms, showing lower reconstruction and detection errors.