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Proceedings Paper

Compressive hyperspectral acquisition and endmember unmixing
Author(s): Ting Sun; Chengbo Li; Yin Zhang; Lina Xu; Kevin Kelly
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Paper Abstract

A new hyperspectral imaging system is constructed based on the idea of compressive sensing (CS). The compressed hyperspectral measurements are acquired and unmixed directly with the proposed algorithm which determines the abundance fractions of endmembers, completely bypassing high-complexity tasks involving the hyperspectral data cube itself. Without the intermediate stage of 3D hyper-cube processing, data reconstruction and unmixing are combined into a single step of much lower complexity. We assume that the involved endmembers' signatures are known and given, from which we then directly compute abundances. We also extend this approach to blind unmixing where endmembers' signatures are not precisely known a priori.

Paper Details

Date Published: 14 September 2011
PDF: 12 pages
Proc. SPIE 8165, Unconventional Imaging, Wavefront Sensing, and Adaptive Coded Aperture Imaging and Non-Imaging Sensor Systems, 81650D (14 September 2011); doi: 10.1117/12.894180
Show Author Affiliations
Ting Sun, Rice Univ. (United States)
Chengbo Li, Rice Univ. (United States)
Yin Zhang, Rice Univ. (United States)
Lina Xu, Rice Univ. (United States)
Kevin Kelly, Rice Univ. (United States)


Published in SPIE Proceedings Vol. 8165:
Unconventional Imaging, Wavefront Sensing, and Adaptive Coded Aperture Imaging and Non-Imaging Sensor Systems
Stanley Rogers; Jean J. Dolne; David P. Casasent; Thomas J. Karr; Victor L. Gamiz, Editor(s)

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