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Proceedings Paper

A novel nonnegative matrix factorization method for hyperspectral unmixing
Author(s): Nan Xu; Huadong Yang
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Paper Abstract

In this paper, we propose a new algorithm integrating pure pixel identification into nonnegative matrix factorization (NMF) model to decompose the mixed pixels existing in hyperspectral imagery. The proposed algorithm employs traditional endmember identification algorithm to search for the pure pixel candidates, and then the principal component analysis is performed on the homogenous pixels which consist of the pure pixel candidates and its neighborhoods to identify the endmembers existing in the real scene. Finally, the known-endmember-based NMF unmixing algorithm is used to generate the other unknown endmembers. The proposed algorithm retains the advantages of both pure pixel identification method and NMF. Experimental results based on simulated and real data sets demonstrate the superiority of the proposed algorithm with respect to other state-of-the-art approaches.

Paper Details

Date Published: 6 May 2019
PDF: 9 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 1106949 (6 May 2019); doi: 10.1117/12.2524186
Show Author Affiliations
Nan Xu, Shenyang Jianzhu Univ. (China)
Huadong Yang, Shenyang Ligong Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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