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Proceedings Paper

A robust endmember constrained non-negative matrix factorization method for hyperspectral unmixing
Author(s): Jinjun Liu
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Paper Abstract

This paper presents a new method based non-negative matrix factorization (NMF) for hyperspectral unmixing, termed robust endmember constrained NMF (RECNMF). The objective function of RECNMF can not only reduce the effect of noise and outliers but also can reduce the size of convex formed by the endmembers and the correlation between the endmembers. The algorithm is solved by the projected gradient method. The effectiveness of RECNMF is illustrated by comparing its performance with the state-of-the-art algorithms in simulated data.

Paper Details

Date Published: 2 March 2016
PDF: 5 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010L (2 March 2016); doi: 10.1117/12.2234687
Show Author Affiliations
Jinjun Liu, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)

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