Sparse superpixel unmixing for exploratory analysis of CRISM hyperspectral images

Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with compact reconnaissance imaging spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel ldquosuperpixelrdquo segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.