中文

Journal of Applied Sciences >

2020 , Vol. 38 >Issue 6: 890 - 905

DOI: https://doi.org/10.3969/j.issn.0255-8297.2020.06.006

Signal and Information Processing

Hyperspectral Sparse Unmixing Based on Local Weighted Low-Rank Prior

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  • 1. College of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, Henan, China;
    2. College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, Jiangsu, China

Received date: 2020-01-04

  Online published: 2020-12-08

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Abstract

In order to fully exploit the intrinsic properties of abundance maps and improve the sparse unmixing accuracy of hyperspectral image, a sparse unmixing method based on local weighted low rank prior is proposed. The low rank prior is mainly based on the fact that the local cubes in hyperspectral images have higher spatial correlation and spectral correlation. The weighted low rank prior can explore the low-dimensional structural features inherent in the local block, effectively suppress the noise and maintain the detailed structure of the data. In combination with the existing total variation regularization and collaborative sparse regularization, the proposed method shows improved ability to exploit the detailed structure of the abundance coefficients, local smoothness and row sparsity. Experimental results on the simulation data and real hyperspectral data show that the proposed method can better maintain the fine details of the data and improve the unmixing accuracy compared with other state-of-the-art methods.

Key words： sparse unmixing; total variation; weighted low rank; collaborative sparse; alternating directions method of multipliers (ADMM)

Cite this article

HUANG Wei, WU Feiyang, SUN Le . Hyperspectral Sparse Unmixing Based on Local Weighted Low-Rank Prior[J]. Journal of Applied Sciences, 2020 , 38(6) : 890 -905 . DOI: 10.3969/j.issn.0255-8297.2020.06.006

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