Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization

doi:10.1109/TCYB.2015.2484324

OPT OpenIR > 光谱成像技术研究室

	Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization
	Yuan, Yuan 1; Lin, Jianzhe 1; Wang, Qi 2,3; Wang, Q (reprint author), Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
作者部门	光学影像学习与分析中心
	2016-12-01
发表期刊	IEEE Transactions on Cybernetics
ISSN	2168-2267
卷号	46 期号:12 页码:2966-2977
产权排序	1
摘要	Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.
文章类型	Article
关键词	Hyperspectral Image (Hsi) Classification Markov Random Field (Mrf) Multitask Sparse Representation
学科领域	Computer Science, Artificial Intelligence
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2015.2484324
收录类别	SCI
关键词[WOS]	DISCRIMINANT-ANALYSIS ; SELECTION ; RECOGNITION ; REGRESSION ; SVM ; RECONSTRUCTION ; SEGMENTATION ; SALIENCY ; MODELS
语种	英语
WOS研究方向	Computer Science
项目资助者	National Basic Research Program of China (Youth 973 Program)(2013CB336500) ; State Key Program of the National Natural Science of China(61232010) ; National Natural Science Foundation of China(61172143 ; Natural Science Foundation Research Project of Shaanxi Province(2015JM6264) ; Fundamental Research Funds for the Central Universities(3102014JC02020G07) ; Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences ; 61379094 ; 61105012)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000388923100023
引用统计	被引频次：163[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/181661/28558
专题	光谱成像技术研究室
通讯作者	Wang, Q (reprint author), Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China.
作者单位	1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 2.Northwestern Polytech Univ, Sch Comp Sci, Xian 710072, Peoples R China 3.Northwestern Polytech Univ, Ctr Opt Imagery Anal & Learning, Xian 710072, Peoples R China
推荐引用方式 GB/T 7714	Yuan, Yuan,Lin, Jianzhe,Wang, Qi,et al. Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization[J]. IEEE Transactions on Cybernetics,2016,46(12):2966-2977.
APA	Yuan, Yuan,Lin, Jianzhe,Wang, Qi,&Wang, Q .(2016).Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.IEEE Transactions on Cybernetics,46(12),2966-2977.
MLA	Yuan, Yuan,et al."Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization".IEEE Transactions on Cybernetics 46.12(2016):2966-2977.

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