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应用科学学报 >

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

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

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基于局部加权低秩先验的高光谱稀疏解混方法

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  • 1. 郑州轻工业大学 计算机与通信工程学院, 河南 郑州 450001;
    2. 南京信息工程大学 计算机与软件学院, 江苏 南京 210044

收稿日期: 2020-01-04

  网络出版日期: 2020-12-08

基金资助

国家自然科学基金（No.61602423，No.61672291，No.61601236，No.61502206）资助

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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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摘要

为了充分挖掘丰度系数的内在本质属性，提升高光谱图像稀疏解混精度，提出一种基于局部加权低秩先验的稀疏解混方法.该低秩先验主要基于这一事实：高光谱图像中的局部立方体块具有较高的相空间关性和光谱相关性.加权的低秩先验能够挖掘局部块内在的低维结构特征，有效地抑制噪声，保持数据的细节结构.该先验联合全变差正则项、协同稀疏正则项，能够更好地刻画丰度系数的细节结构、局部平滑性以及行稀疏性.利用模拟数据和真实高光谱数据进行的实验表明，所提方法与现有方法相比能够更好地保持数据的细节信息，提升解混精度.

关键词： 稀疏性解混; 全变差; 加权低秩; 协同稀疏; 交替方向乘子法

本文引用格式

黄伟, 伍飞扬, 孙乐 . 基于局部加权低秩先验的高光谱稀疏解混方法[J]. 应用科学学报, 2020 , 38(6) : 890 -905 . DOI: 10.3969/j.issn.0255-8297.2020.06.006

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)

参考文献

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