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Remote Sens. 2017, 9(10), 1074;

Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing

College of Electrical Engineering, Zhejiang University, No.38, Zheda Road, Xihu District, Hangzhou 310027, China
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
Author to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 16 October 2017 / Accepted: 18 October 2017 / Published: 21 October 2017
(This article belongs to the Special Issue Hyperspectral Imaging and Applications)
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Hyperspectral unmixing aims to estimate a set of endmembers and corresponding abundances in pixels. Nonnegative matrix factorization (NMF) and its extensions with various constraints have been widely applied to hyperspectral unmixing. L 1 / 2 and L 2 regularizers can be added to NMF to enforce sparseness and evenness, respectively. In practice, a region in a hyperspectral image may possess different sparsity levels across locations. The problem remains as to how to impose constraints accordingly when the level of sparsity varies. We propose a novel nonnegative matrix factorization with data-guided constraints (DGC-NMF). The DGC-NMF imposes on the unknown abundance vector of each pixel with either an L 1 / 2 constraint or an L 2 constraint according to its estimated mixture level. Experiments on the synthetic data and real hyperspectral data validate the proposed algorithm. View Full-Text
Keywords: nonnegative matrix factorization; data-guided constraints; sparseness; evenness nonnegative matrix factorization; data-guided constraints; sparseness; evenness

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Huang, R.; Li, X.; Zhao, L. Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing. Remote Sens. 2017, 9, 1074.

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