On the unmixing of MEx/OMEGA hyperspectral data

TL;DR

This paper compares three estimators for supervised linear unmixing of MEx/OMEGA hyperspectral data, highlighting the Bayesian MAP estimator's superior performance and potential for large-scale ice and mineral detection.

Contribution

It provides a comparative analysis of unmixing estimators, demonstrating the effectiveness of the Bayesian MAP approach for physically interpretable hyperspectral unmixing.

Findings

Bayesian MAP estimator outperforms others in accuracy

MAP estimator offers a good balance between complexity and performance

Suitable for large hyperspectral datasets in ice and mineral detection

Abstract

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.