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Proceedings Paper

A novel edge-preserving nonnegative matrix factorization method for spectral unmixing
Author(s): Wenxing Bao; Ruishi Ma
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Paper Abstract

Spectral unmixing technique is one of the key techniques to identify and classify the material in the hyperspectral image processing. A novel robust spectral unmixing method based on nonnegative matrix factorization(NMF) is presented in this paper. This paper used an edge-preserving function as hypersurface cost function to minimize the nonnegative matrix factorization. To minimize the hypersurface cost function, we constructed the updating functions for signature matrix of end-members and abundance fraction respectively. The two functions are updated alternatively. For evaluation purpose, synthetic data and real data have been used in this paper. Synthetic data is used based on end-members from USGS digital spectral library. AVIRIS Cuprite dataset have been used as real data. The spectral angle distance (SAD) and abundance angle distance(AAD) have been used in this research for assessment the performance of proposed method. The experimental results show that this method can obtain more ideal results and good accuracy for spectral unmixing than present methods.

Paper Details

Date Published: 9 December 2015
PDF: 6 pages
Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170C (9 December 2015); doi: 10.1117/12.2228424
Show Author Affiliations
Wenxing Bao, Beifang Univ. of Nationalities (China)
Ruishi Ma, Beifang Univ. of Nationalities (China)


Published in SPIE Proceedings Vol. 9817:
Seventh International Conference on Graphic and Image Processing (ICGIP 2015)
Yulin Wang; Xudong Jiang, Editor(s)

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