  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.
