Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection

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

	Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection
	Yang, Xi 1; Gao, Xinbo 1; Tao, Dacheng2,3 ; Li, Xuelong4 ; Han, Bing 5; Li, Jie 5
刊名	ieee transactions on neural networks and learning systems
	2016
卷号	27 期号:1 页码:32-46
关键词	Auroral substorm detection sequence motion analysis shape constraint sparse and low-rank decomposition (SLD)
ISSN号	2162-237x
通讯作者	yang, x
产权排序	4
英文摘要	an auroral substorm is an important geophysical phenomenon that reflects the interaction between the solar wind and the earth's magnetosphere. detecting substorms is of practical significance in order to prevent disruption to communication and global positioning systems. however, existing detection methods can be inaccurate or require time-consuming manual analysis and are therefore impractical for large-scale data sets. in this paper, we propose an automatic auroral substorm detection method based on a shape-constrained sparse and low-rank decomposition (scsld) framework. our method automatically detects real substorm onsets in large-scale aurora sequences, which overcomes the limitations of manual detection. to reduce noise interference inherent in current sld methods, we introduce a shape constraint to force the noise to be assigned to the low-rank part (stationary background), thus ensuring the accuracy of the sparse part (moving object) and improving the performance. experiments conducted on aurora sequences in solar cycle 23 (1996-2008) show that the proposed scsld method achieves good performance for motion analysis of aurora sequences. moreover, the obtained results are highly consistent with manual analysis, suggesting that the proposed automatic method is useful and effective in practice.
学科主题	computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence ; computer science, hardware & architecture ; computer science, theory & methods ; engineering, electrical & electronic
研究领域[WOS]	computer science ; engineering
关键词[WOS]	moving object detection ; level set method ; background subtraction ; matrix factorization ; detection algorithm ; video surveillance ; oval segmentation ; image retrieval ; subspace ; phase
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000367253200004
公开日期	2016-02-25
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/27739]
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.Xidian Univ, Sch Elect Engn, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Intelligent Syst, Sydney, NSW 2007, Australia 3.Univ Technol Sydney, Fac Engn & Informat Technol, Sydney, NSW 2007, Australia 4.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Peoples R China 5.Xidian Univ, Sch Elect Engn, Video & Image Proc Syst Lab, Xian 710071, Peoples R China
推荐引用方式 GB/T 7714	Yang, Xi,Gao, Xinbo,Tao, Dacheng,et al. Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection[J]. ieee transactions on neural networks and learning systems,2016,27(1):32-46.
APA	Yang, Xi,Gao, Xinbo,Tao, Dacheng,Li, Xuelong,Han, Bing,&Li, Jie.(2016).Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection.ieee transactions on neural networks and learning systems,27(1),32-46.
MLA	Yang, Xi,et al."Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection".ieee transactions on neural networks and learning systems 27.1(2016):32-46.