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Proceedings Paper

L1 unmixing and its application to hyperspectral image enhancement
Author(s): Zhaohui Guo; Todd Wittman; Stanley Osher
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Paper Abstract

Because hyperspectral imagery is generally low resolution, it is possible for one pixel in the image to contain several materials. The process of determining the abundance of representative materials in a single pixel is called spectral unmixing. We discuss the L1 unmixing model and fast computational approaches based on Bregman iteration. We then use the unmixing information and Total Variation (TV) minimization to produce a higher resolution hyperspectral image in which each pixel is driven towards a "pure" material. This method produces images with higher visual quality and can be used to indicate the subpixel location of features.

Paper Details

Date Published: 27 April 2009
PDF: 9 pages
Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341M (27 April 2009); doi: 10.1117/12.818245
Show Author Affiliations
Zhaohui Guo, Univ. of California, Los Angeles (United States)
Todd Wittman, Univ. of California, Los Angeles (United States)
Stanley Osher, Univ. of California, Los Angeles (United States)


Published in SPIE Proceedings Vol. 7334:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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