Image and Signal Processing Methods

Nonlinear hyperspectral unmixing based on sparse non-negative matrix factorization

[+] Author Affiliations
Jing Li, Xiaorun Li

Zhejiang University, College of Electrical Engineering, Room C421, Zhiquan Building, No. 38, Zheda Road, Xihu District, Hangzhou, Zhejiang Province, 310027, China

Liaoying Zhao

Hangzhou Dianzi University, Institute of Computer Application Technology, Hangzhou, Zhejiang 310018, China

J. Appl. Remote Sens. 10(1), 015003 (Jan 19, 2016). doi:10.1117/1.JRS.10.015003
History: Received August 21, 2015; Accepted December 4, 2015
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Abstract.  Hyperspectral unmixing aims at extracting pure material spectra, accompanied by their corresponding proportions, from a mixed pixel. Owing to modeling more accurate distribution of real material, nonlinear mixing models (non-LMM) are usually considered to hold better performance than LMMs in complicated scenarios. In the past years, numerous nonlinear models have been successfully applied to hyperspectral unmixing. However, most non-LMMs only think of sum-to-one constraint or positivity constraint while the widespread sparsity among real materials mixing is the very factor that cannot be ignored. That is, for non-LMMs, a pixel is usually composed of a few spectral signatures of different materials from all the pure pixel set. Thus, in this paper, a smooth sparsity constraint is incorporated into the state-of-the-art Fan nonlinear model to exploit the sparsity feature in nonlinear model and use it to enhance the unmixing performance. This sparsity-constrained Fan model is solved with the non-negative matrix factorization. The algorithm was implemented on synthetic and real hyperspectral data and presented its advantage over those competing algorithms in the experiments.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Topics

Algorithms ; Matrices

Citation

Jing Li ; Xiaorun Li and Liaoying Zhao
"Nonlinear hyperspectral unmixing based on sparse non-negative matrix factorization", J. Appl. Remote Sens. 10(1), 015003 (Jan 19, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.015003


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