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

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization
	Yuan, Yuan 1; Lin, Jianzhe 1; Wang, Qi 2,3
刊名	ieee transactions on cybernetics
	2016-12-01
卷号	46 期号:12 页码:2966-2977
关键词	Hyperspectral image (HSI) classification Markov random field (MRF) multitask sparse representation
ISSN号	2168-2267
通讯作者	wang, q (reprint author), northwestern polytech univ, sch comp sci, xian 710072, peoples r china.
产权排序	1
英文摘要	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.
学科主题	computer science, artificial intelligence ; computer science, cybernetics
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, cybernetics
研究领域[WOS]	computer science
关键词[WOS]	discriminant-analysis ; selection ; recognition ; regression ; svm ; reconstruction ; segmentation ; saliency ; models
收录类别	SCI
语种	英语
WOS记录号	WOS:000388923100023
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/28558]
专题	西安光学精密机械研究所_光学影像学习与分析中心
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. 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.(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.