On the unmixing of MEx/OMEGA hyperspectral data
Schmidt, Frédéric; Daglis, Ioannis A.; Themelis, Konstantinos E.; Sykioti, Olga; Rontogiannis, Athanasios A.; Koutroumbas, Konstantinos D.
Greece, France
Abstract
This paper 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.