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Proceedings Paper

Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images
Author(s): Yue Yu; Weidong Sun
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Paper Abstract

Endmember extraction and spectral unmixing is a very challenging task in multispectral/hyperspectral image processing due to the incompleteness of information. In this paper, a new method for endmember extraction and spectral unmixing of hyperspectral images is proposed, which is called as minimum distance constrained nonnegative matrix factorization (MDC-NMF). After being compared with a newly developed method named MVC-NMF, MDC-NMF not only has been demonstrated more reasonable in theory but also shows promising results in the experiments.

Paper Details

Date Published: 14 November 2007
PDF: 9 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679015 (14 November 2007); doi: 10.1117/12.748379
Show Author Affiliations
Yue Yu, Tsinghua Univ. (China)
Weidong Sun, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications

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