NDLI: Robust unmixing of hyperspectral images: Application to Mars

Content Provider	IEEE Xplore Digital Library
Author	Parente, M. Mustard, J.F. Murchie, S. Seelos, F.
Copyright Year	2011
Description	Author affiliation: Brown University, Dept. of Geological Sciences, Providence, RI 02912, USA (Parente, M.; Mustard, J.F.) \|\| Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, USA (Murchie, S.; Seelos, F.)
Abstract	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 hyperspectral 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.
Starting Page	1291
Ending Page	1294
File Size	1142987
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049436
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mars Hyperspectral imaging Noise Manuals Reconnaissance Absorption Imaging five One two three four
Content Type	Text
Resource Type	Article

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