A robust endmember constrained non-negative matrix factorization method for hyperspectral unmixing

Jinjun Liu

doi:10.1117/12.2234687

2 March 2016 A robust endmember constrained non-negative matrix factorization method for hyperspectral unmixing

Jinjun Liu

Proceedings Volume 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015); 99010L (2016) https://doi.org/10.1117/12.2234687
Event: 2015 ISPRS International Conference on Computer Vision in Remote Sensing, 2015, Xiamen, China

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.

Citation Download Citation

Jinjun Liu "A robust endmember constrained non-negative matrix factorization method for hyperspectral unmixing", Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010L (2 March 2016); https://doi.org/10.1117/12.2234687

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