Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images

doi:10.1109/TGRS.2015.2461653

OPT OpenIR > 光谱成像技术研究室

	Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images
	Gong, Maoguo 1; Zhang, Mingyang 1; Yuan, Yuan2
作者部门	光学影像学习与分析中心
	2016
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	01962892
卷号	54 期号:1 页码:544-557
产权排序	2
摘要	Band selection is an important preprocessing step for hyperspectral image processing. Many valid criteria have been proposed for band selection, and these criteria model band selection as a single-objective optimization problem. In this paper, a novel multiobjective model is first built for band selection. In this model, two objective functions with a conflicting relationship are designed. One objective function is set as information entropy to represent the information contained in the selected band subsets, and the other one is set as the number of selected bands. Then, based on this model, a new unsupervised band selection method called multiobjective optimization band selection (MOBS) is proposed. In the MOBS method, these two objective functions are optimized simultaneously by a multiobjective evolutionary algorithm to find the best tradeoff solutions. The proposed method shows two unique characters. It can obtain a series of band subsets with different numbers of bands in a single run to offer more options for decision makers. Moreover, these band subsets with different numbers of bands can communicate with each other and have a coevolutionary relationship, which means that they can be optimized in a cooperative way. Since it is unsupervised, the proposed algorithm is compared with some related and recent unsupervised methods for hyperspectral image band selection to evaluate the quality of the obtained band subsets. Experimental results show that the proposed method can generate a set of band subsets with different numbers of bands in a single run and that these band subsets have a stable good performance on classification for different data sets.
文章类型	Article
关键词	Band Selection Evolutionary Algorithm (Ea) Hyperspectral Image Multiobjective Optimization
学科领域	Geochemistry & Geophysics
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/TGRS.2015.2461653
收录类别	SCI ; EI
关键词[WOS]	EXTREME LEARNING-MACHINE ; PRINCIPAL COMPONENTS TRANSFORM ; DIMENSIONALITY REDUCTION ; MUTUAL INFORMATION ; CLONAL SELECTION ; CLASSIFICATION ; ALGORITHM ; ACCURACY ; REMOVAL ; QUALITY
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
项目资助者	National Natural Science Foundation of China(61273317 ; National Program for Support of Top-notch Young Professionals of China ; Specialized Research Fund for the Doctoral Program of Higher Education(20130203110011) ; Fundamental Research Fund for the Central Universities(K5051202053) ; 61422209 ; 61172143)
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000364833900042
引用统计	被引频次：131[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/27497
专题	光谱成像技术研究室
作者单位	1.Xidian Univ, Key Lab Intelligent Percept & Image Understanding, Int Res Ctr Intelligent Percept & Computat, Minist Educ, Xian 710071, Peoples R China 2.Chinese Acad Sci, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
推荐引用方式 GB/T 7714	Gong, Maoguo,Zhang, Mingyang,Yuan, Yuan. Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2016,54(1):544-557.
APA	Gong, Maoguo,Zhang, Mingyang,&Yuan, Yuan.(2016).Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,54(1),544-557.
MLA	Gong, Maoguo,et al."Unsupervised Band Selection Based on Evolutionary Multiobjective Optimization for Hyperspectral Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 54.1(2016):544-557.

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