期刊
NEUROCOMPUTING
卷 273, 期 -, 页码 57-67出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2017.07.059
关键词
Airborne remote sensing; Hyperspectral image classification; Band selection; Combinational optimization algorithm; Chaotic binary coded gravitational search algorithm
资金
- National Science & Technology Pillar Program [2014BAL05B07]
- National Key Research & Development Program of China [2016YFC0600210]
- Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation [2016NGCM07]
Band selection is one of the most important topics in hyperspectral image classification for irrelevant band information and the high correlation between the adjacent bands. The main concern is to obtain the compact and effective bands to classify the image with the least impact for the classification accuracy. In general, band selection could be seen as a combinatorial optimization problem through defining an objective function based on the number of bands and classification accuracy. Therefore, in the paper, a novel band selection method based on a chaotic binary coded gravitational search algorithm (CBGSA) is proposed to reduce the dimensionality of airborne hyperspectral images. The proposed method is also compared with that of genetic algorithm (GA), binary coded particle swarm optimization (BPSO) algorithm, binary coded differential evolution (BDE) algorithm and binary coded cuckoo search (BCS) algorithm on some airborne hyperspectral images; furthermore, it is also compared with some other existing techniques such as Relief-F algorithm, minimum Redundancy Maximum Relevance (mRMR) criterion, and the optimum index (OI) criterion for a comprehensive comparison. Experimental results display that the proposed method is robust, adaptive and might be applied for practical work of airborne hyperspectral image classification. (C) 2017 Elsevier B.V. All rights reserved.
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