A Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization Algorithm for Hyperspectral Endmember Extraction

被引:27
|
作者
Xu, Mingming [1 ,2 ,3 ]
Zhang, Liangpei [3 ]
Du, Bo [4 ]
Zhang, Lefei [4 ]
Fan, Yanguo [1 ]
Song, Dongmei [1 ]
机构
[1] China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China
[2] Qingdao Natl Lab Marine Sci & Technol, Lab Marine Mineral Resources, Qingdao 266071, Peoples R China
[3] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China
[4] Wuhan Univ, Sch Comp, Wuhan 430072, Peoples R China
基金
中国国家自然科学基金;
关键词
Quantum-Behaved Particle Swarm Optimization (QPSO); quantum-behaved; endmember extraction; hyperspectral image; SPARSE REGRESSION; SELECTION;
D O I
10.3390/rs9030197
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The endmember extraction algorithm, which selects a collection of pure signature spectra for different materials, plays an important role in hyperspectral unmixing. In this paper, the endmember extraction algorithm is described as a combinatorial optimization problem and a novel Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization (MOAQPSO) algorithm is proposed. The proposed approach employs Quantum-Behaved Particle Swarm Optimization (QPSO) to find endmembers with good performances. To the best of our knowledge, this is the first time that QPSO has been introduced into hyperspectral endmember extraction. In order to follow the law of particle movement, a high-dimensional particle definition is proposed. In addition, in order to avoid falling into a local optimum, a mutation operation is used to increase the population diversity. The proposed MOAQPSO algorithm was evaluated on both synthetic and real hyperspectral data sets. The experimental results indicated that the proposed method obtained better results than other state-of-the-art algorithms, including Vertex Component Analysis (VCA), N-FINDR, and Discrete Particle Swarm Optimization (D-PSO).
引用
收藏
页数:18
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