Sparse unmixing of hyperspectral images based on large-scale many-objective optimization algorithm

被引：0
|
作者：
Bi X. [1 ,2 ]
Zhou Z. [2 ]
机构：
[1] School of Information Engineering, Minzu University of China, Beijing
[2] Department of Information and Communication Engineering, Harbin Engineering University, Harbin
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2019年 / 40卷 / 07期
关键词：
Hyperspectral image; Knee point area; Large-scale many-objective evolutionary optimization (LMEA) algorithm; Linear spectral unmixing model; Multi-objective optimization; Sparse unmixing;
D O I：
10.11990/jheu.201807075
中图分类号：
学科分类号：
摘要：
The existing multi-objective sparse unmixing algorithm has the defect of random grouping strategy and simplicity of the knee point selection, which leads to low accuracy of the hyperspectral data unmixing. Considering this problem, this paper proposes a hyperspectral sparse unmixing algorithm based on the large-scale many-objective evolutionary optimization (LMEA) algorithm. Based on the decision variable grouping strategy of the LMEA, a constrained knee point area selection strategy is used to obtain the abundance optimal solution to improve the unmixing accuracy. Experiments on simulated and real hyperspectral data show that the proposed algorithm greatly improved the unmixing accuracy. Compared with other algorithms, the abundance map edge details obtained by this algorithm were better processed and the anti-noise performance was stronger; this verifies the effectiveness and advancement of the proposed algorithm. © 2019, Editorial Department of Journal of HEU. All right reserved.
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页码：1354 / 1360
页数：6
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