Automated algorithms to identify and locate grains of specific composition for NIR hyperspectral microscopes: Application to the MicrOmega instrument onboard ExoMars

Abstract

MicrOmega is an NIR hyperspectral microscope that has been selected as part of the ExoMars rover payload. We demonstrate here that this instrument has the capability through automated algorithms to identify and locate grains of interest (composition-wise) within a collected sample that could be further targeted by other instruments within the ExoMars payload. Results will also be used to perform an optimized compression of the data, with regard to the relevance of the different grains, in order to limit the volume of downloaded data. The algorithms that we have developed, using combined spectral criteria and different tests to avoid false positive, have proven to be both highly robust and efficient. They will be used onboard ExoMars rover, and could also be adapted for future robotic missions involving hyperspectral microscopes.

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