On the unmixing of MEx/OMEGA hyperspectral data

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.

References in corpus (2)

• Implementation strategies for hyperspectral unmixing using Bayesian source separation
• On the unmixing of MEx/OMEGA hyperspectral data

Cited by in corpus (7)

• Nonlinear hyperspectral unmixing with robust nonnegative matrix factorization
• Unsupervised Nonlinear Spectral Unmixing based on a Multilinear Mixing Model
• Minerals detection for hyperspectral images using adapted linear unmixing: LinMin
• On the unmixing of MEx/OMEGA hyperspectral data
• Spectral unmixing for exoplanet direct detection in hyperspectral data
• Provably robust blind source separation of linear-quadratic near-separable mixtures
• Fast Spectral Unmixing based on Dykstra's Alternating Projection