Minimum distance constrained nonnegative matrix factorization for the endmember extraction of hyperspectral images
نویسندگان
چکیده
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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