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, spar...

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Bibliographic Details
Published in2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing pp. 1 - 4
Main Authors Thompson, D.R., Castao, R., Gilmore, M.S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Automatic testing
CRISM
Data mining
Hyperspectral Images
Hyperspectral imaging
Image analysis
Image segmentation
Mars
Minerals
Reconnaissance
Sparse Bayesian Unmixing
Spectroscopy
Superpixels
Online AccessGet full text
ISBN9781424446865
1424446864
ISSN2158-6268
DOI10.1109/WHISPERS.2009.5289045

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Summary: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.
ISBN:9781424446865
1424446864
ISSN:2158-6268
DOI:10.1109/WHISPERS.2009.5289045