Hyperspectral Images Unmixing Based on Nonnegative Matrix Factorization Optimized by Salp Swarm Algorithm

被引：0
|
作者：
Liu S. [1 ]
Jia Z. [1 ]
Chen L. [2 ,3 ]
Guo Y. [1 ]
机构：
[1] College of Electronic Information Engineering, Hebei University of Technology, Tianjin
[2] College of Precision Instrument and Opto-Electronic Engineering, Tianjin University, Tianjin
[3] College of Information Engineering, Tianjin University of Commerce, Tianjin
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2019年 / 31卷 / 02期
关键词：
Hyperspectral image; Nonlinear unmixing; Robust linear mixing model; Salp swarm algorithm; Swarm intelligence optimization;
D O I：
10.3724/SP.J.1089.2019.17189
中图分类号：
学科分类号：
摘要：
In order to solve the problem that robust nonnegative matrix factorization is sensitive to the initial values and usually trapped in a local optimum when applied to hyperspectral images processing, a new hyperspectral image unmixing algorithm based on robust nonnegative matrix factorization optimized by salp swarm algorithm is proposed. The algorithm is based on robust linear mixed model. Under the framework of robust nonnegative matrix factorization, the global search capability is enhanced by replacing the multiplicative iteration strategy with the salp swarm optimization algorithm, the global optimal solution satisfying the objective function is randomly searched in the constrained space. And then, the hyperspectral image unmixing is effectively fulfilled. The experimental results on synthetic data and real remote sensing data indicate that the algorithm proposed can effectively avoid the limitation of RNMF falling into the local optimal solution, which has better performance of unmixing. © 2019, Beijing China Science Journal Publishing Co. Ltd. All right reserved.
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页码：315 / 323
页数：8
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