Evaluation of bayesian hyperspectral image segmentation with a discriminative class learning

A Bayesian segmentation approach for hyperspectral images is introduced in this paper. The method improves the classification performance of discriminative classifiers by adding contextual information in the form of spatial dependencies. The technique herein presented builds the class densities based on Fast Sparse Multinomial Logistic Regression and enforces spacial continuity by adopting a Multi-Level Logistic Markov-Gibs prior. State-of-art performance of the proposed approach is illustrated in a set of experimental comparisons with recently introduced hyperspectral classification/segmentation methods.