Constrained non-negative matrix factorization algorithm combined with spatial homogeneous area analysis for hyperspectral unmixing

Haifeng Tian; Ying Zhan; Lu Wang; Dan Hu; Xianchuan Yu

doi:10.1117/1.JRS.13.018504

28 March 2019 Constrained non-negative matrix factorization algorithm combined with spatial homogeneous area analysis for hyperspectral unmixing

Haifeng Tian, Ying Zhan, Lu Wang, Dan Hu, Xianchuan Yu

Author Affiliations +

Funded by: National Natural Science Foundation of China (NSFC), Ministry of Land and Resources of the People's Republic of China

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 018504 (March 2019). https://doi.org/10.1117/1.JRS.13.018504

Abstract

The phenomenon of mixed pixels is serious in hyperspectral images because of its low spatial resolution; so, unmixing mixed pixels is very important for further application of hyperspectral images. The geometrical description in the characteristic space and the sparseness of the abundance matrix are the most important characteristics of the hyperspectral data, as well as the spatial construction information. Traditional algorithms always consider one characteristic and neglect the other. We propose the endmember initials determined by spatial homogeneous area analysis-minimum volume and L_1/2 regularization sparseness-constrained non-negative matrix factorization algorithm, considering all the above characteristics. In estimating the initial endmember matrix, spatial homogeneous area analysis is performed on endmember pixels calculated by vertex component analysis to achieve the goal of accelerating the algorithm’s convergence. At the same time, we join the minimum volume endmember constraint and L_1/2 regularization abundance constraint of stronger sparseness as the final model of our algorithm. The experimental results of both the synthetic and the real data show that, compared with some classical algorithms, the proposed algorithm has a higher accuracy in hyperspectral unmixing.

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Haifeng Tian, Ying Zhan, Lu Wang, Dan Hu, and Xianchuan Yu "Constrained non-negative matrix factorization algorithm combined with spatial homogeneous area analysis for hyperspectral unmixing," Journal of Applied Remote Sensing 13(1), 018504 (28 March 2019). https://doi.org/10.1117/1.JRS.13.018504

Received: 13 July 2018; Accepted: 22 February 2019; Published: 28 March 2019

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