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Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral ImagesFast 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 "superpixel" 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.
Document ID
20150008444
Acquisition Source
Jet Propulsion Laboratory
Document Type
Conference Paper
External Source(s)
Authors
Thompson, David R.
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Castano, Rebecca
(Jet Propulsion Lab., California Inst. of Tech. Pasadena, CA, United States)
Gilmore, Martha S.
(Wesleyan Univ. Middletown, CT, United States)
Date Acquired
May 19, 2015
Publication Date
August 26, 2009
Subject Category
Statistics And Probability
Instrumentation And Photography
Meeting Information
Meeting: IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)
Location: Grenoble
Country: France
Start Date: August 26, 2009
End Date: August 28, 2009
Sponsors: Institute of Electrical and Electronics Engineers, Institut National Polytechnique
Distribution Limits
Public
Copyright
Other
Keywords
Image Segmentation
Sparse Bayesian Unmixing

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