Robust unmixing of hyperspectral images: Application to Mars

Planetary missions such as the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) can benefit from the use of automatic approaches and statistical learning techniques due to the amount of data involved. Thanks to its high sensor resolution, CRISM data volumes overwhelm scientists capacity for exhaustive manual analysis. Planetary investigations would benefit from an automated process that could identify the unique spectral signatures present in a CRISM scene and store them for further examination or interpretation. If installed aboard an orbital system, such a tool could relieve transmission constraints for high-bandwidth hyper spectral datasets by giving priority to the most informative data products. This paper introduces an algorithm that extracts image endmembers of a CRISM scene, which can be used as the scene concise mineralogical representation for cataloging purposes, in addition to existing browse products and parameter maps. The approach uses robust techniques, resilient to CRISM noise. This work benefits from the results of previous efforts [6] and it is currently being extended to other hyperspectral datasets.