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Article

Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing

1
College of Electrical Engineering, Zhejiang University, No.38, Zheda Road, Xihu District, Hangzhou 310027, China
2
School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(10), 1074; https://doi.org/10.3390/rs9101074
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)
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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MDPI and ACS Style

Huang, R.; Li, X.; Zhao, L. Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing. Remote Sens. 2017, 9, 1074. https://doi.org/10.3390/rs9101074

AMA Style

Huang R, Li X, Zhao L. Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing. Remote Sensing. 2017; 9(10):1074. https://doi.org/10.3390/rs9101074

Chicago/Turabian Style

Huang, Risheng; Li, Xiaorun; Zhao, Liaoying. 2017. "Nonnegative Matrix Factorization With Data-Guided Constraints For Hyperspectral Unmixing" Remote Sens. 9, no. 10: 1074. https://doi.org/10.3390/rs9101074

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