On the unmixing of MEx/OMEGA hyperspectral data
  This article presents a comparative study of three different types of estimators used for supervised linear unmixing of two MEx/OMEGA hyperspectral cubes. The algorithms take into account the constraints of the abundance fractions, in order to get physically interpretable results. Abundance maps show that the Bayesian maximum a posteriori probability (MAP) estimator proposed in Themelis and Rontogiannis (2008) outperforms the other two schemes, offering a compromise between complexity and estimation performance. Thus, the MAP estimator is a candidate algorithm to perform ice and minerals detection on large hyperspectral datasets. 
Konstantinos E. Themelis, Fr\'ed\'eric Schmidt, Olga Sykioti,   Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas, Ioa...
astro-ph.IM astro-ph.EP physics.space-ph stat.AP