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Proceedings Paper

Sparse demixing
Author(s): John B. Greer
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Paper Abstract

The linear mixture model for hyperspectral images assumes that all the image spectra lie on a high-dimensional simplex with corners called endmembers. Given the set of endmembers, one typically calculates fractional abundances for each pixel using constrained least squares. This method likely reconstructs the spectra as combinations of most, if not all, the endmembers. We instead assume that pixels are combinations of only a few of the endmembers, yielding sparse abundance vectors. We introduce a new method, similar to Matching Pursuit (MP) from the signal processing literature, to calculate these sparse abundances. We combine this sparse demixing algorithm with dictionary learning methods to automatically calculate endmembers for a provided set of spectra. We apply our method to an AVIRIS image of Cuprite, NV, for which we compare our endmembers with spectral signatures from the USGS spectral library.

Paper Details

Date Published: 13 May 2010
PDF: 12 pages
Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI, 76951O (13 May 2010); doi: 10.1117/12.850346
Show Author Affiliations
John B. Greer, National Geospatial-Intelligence Agency (United States)


Published in SPIE Proceedings Vol. 7695:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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