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Proceedings Paper

Nonlinear hyperspectral unmixing based on constrained multiple kernel NMF
Author(s): Jiantao Cui; Xiaorun Li; Liaoying Zhao
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Paper Abstract

Nonlinear spectral unmixing constitutes an important field of research for hyperspectral imagery. An unsupervised nonlinear spectral unmixing algorithm, namely multiple kernel constrained nonnegative matrix factorization (MKCNMF) is proposed by coupling multiple-kernel selection with kernel NMF. Additionally, a minimum endmemberwise distance constraint and an abundance smoothness constraint are introduced to alleviate the uniqueness problem of NMF in the algorithm. In the MKCNMF, two problems of optimizing matrices and selecting the proper kernel are jointly solved. The performance of the proposed unmixing algorithm is evaluated via experiments based on synthetic and real hyperspectral data sets. The experimental results demonstrate that the proposed method outperforms some existing unmixing algorithms in terms of spectral angle distance (SAD) and abundance fractions.

Paper Details

Date Published: 22 May 2014
PDF: 6 pages
Proc. SPIE 9124, Satellite Data Compression, Communications, and Processing X, 91240N (22 May 2014); doi: 10.1117/12.2057580
Show Author Affiliations
Jiantao Cui, Zhejiang Univ. (China)
Xiaorun Li, Zhejiang Univ. (China)
Liaoying Zhao, Hangzhou Dianzi Univ. (China)


Published in SPIE Proceedings Vol. 9124:
Satellite Data Compression, Communications, and Processing X
Bormin Huang; Chein-I Chang; José Fco. López, Editor(s)

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