Nonlinear hyperspectral unmixing based on sparse non-negative matrix factorization

Jing Li; Xiaorun Li; Liaoying Zhao

doi:10.1117/1.JRS.10.015003

19 January 2016 Nonlinear hyperspectral unmixing based on sparse non-negative matrix factorization

Jing Li, Xiaorun Li, Liaoying Zhao

Author Affiliations +

Funded by: Zhejiang Provincial Natural Science Foundation of China, National Natural Science Foundation of China (NSFC), National Nature Science Foundation of China, Natural Science Foundation of China

Journal of Applied Remote Sensing, Vol. 10, Issue 1, 015003 (January 2016). https://doi.org/10.1117/1.JRS.10.015003

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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Jing Li, Xiaorun Li, and Liaoying Zhao "Nonlinear hyperspectral unmixing based on sparse non-negative matrix factorization," Journal of Applied Remote Sensing 10(1), 015003 (19 January 2016). https://doi.org/10.1117/1.JRS.10.015003

Published: 19 January 2016

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