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.
@article{arxiv.1112.1527,
title = {On the unmixing of MEx/OMEGA hyperspectral data},
author = {Konstantinos E. Themelis and Frédéric Schmidt and Olga Sykioti and Athanasios A. Rontogiannis and Konstantinos D. Koutroumbas and Ioannis A. Daglis},
journal= {arXiv preprint arXiv:1112.1527},
year = {2011}
}