L1 unmixing and its application to hyperspectral image enhancement

Zhaohui Guo; Todd Wittman; Stanley Osher

doi:10.1117/12.818245

27 April 2009 L1 unmixing and its application to hyperspectral image enhancement

Zhaohui Guo, Todd Wittman, Stanley Osher

Proceedings Volume 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV; 73341M (2009) https://doi.org/10.1117/12.818245
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Zhaohui Guo, Todd Wittman, and Stanley Osher "L1 unmixing and its application to hyperspectral image enhancement", Proc. SPIE 7334, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 73341M (27 April 2009); https://doi.org/10.1117/12.818245

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