Implementation strategies for hyperspectral unmixing using Bayesian source separation
Résumé
Bayesian source separation with positivity constraint (BPSS) is a useful unsupervised approach for hyperspectral data unmixing. The main interest of this approach is to ensure the positivity of the unmixed component spectra and abundances. Moreover, a recent extension has been proposed to impose the sum-to-one (full additivity) constraint to the estimated abundances. Unfortunately, even if positivity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms is limited by high computation time and large memory requirements since these Bayesian algorithms employ Markov Chain Monte Carlo methods. This article introduces an implementation strategy which allows one to apply such algorithms to a full hyperspectral image, as typical in Earth and Planetary Science, with reduced computation cost. We study the effect of pixel selection as a preprocessing step and discuss the impact of such preprocessing on the relevance of the estimated component spectra and abundance maps as well as on the whole computation times. For that purpose, we use two different datasets: a synthetic one and a real hyperspectral image from Mars.