Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images

Yue Yu; Weidong Sun

doi:10.1117/12.748379

14 November 2007 Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images

Yue Yu, Weidong Sun

Author Affiliations +

Proceedings Volume 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications; 679015 (2007) https://doi.org/10.1117/12.748379
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

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.

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Yue Yu and Weidong Sun "Minimum distance constrained non-negative matrix factorization for the endmember extraction of hyperspectral images", Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 679015 (14 November 2007); https://doi.org/10.1117/12.748379

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