| Shape-Constrained Sparse and Low-Rank Decomposition for Auroral Substorm Detection | |
Yang, Xi1; Gao, Xinbo1; Tao, Dacheng2,3 ; Li, Xuelong4; Han, Bing5; Li, Jie5; Yang, X
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| 作者部门 | 光学影像学习与分析中心 |
| 2016 | |
| 发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
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| ISSN | 2162-237X |
| 卷号 | 27期号:1页码:32-46 |
| 产权排序 | 4 |
| 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. |
| 文章类型 | Article |
| 关键词 | Auroral Substorm Detection Sequence Motion Analysis Shape Constraint Sparse And Low-rank Decomposition (Sld) |
| 学科领域 | Computer Science, Artificial Intelligence |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TNNLS.2015.2411613 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | MOVING OBJECT DETECTION ; LEVEL SET METHOD ; BACKGROUND SUBTRACTION ; MATRIX FACTORIZATION ; DETECTION ALGORITHM ; VIDEO SURVEILLANCE ; OVAL SEGMENTATION ; IMAGE RETRIEVAL ; SUBSPACE ; PHASE |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| 项目资助者 | National Basic Research Program of China (973 Program)(2012CB316400) ; National Natural Science Foundation of China(61125204 ; Australian Research Council Projects(FT-130101457 ; Fundamental Research Funds for the Central Universities(BDZ021403 ; Microsoft Research Asia Project(FY13-RES-OPP-034) ; Program for Changjiang Scholars and Innovative Research Team in University of China(IRT13088) ; Shaanxi Innovative Research Team for Key Science and Technology(2012KCT-02) ; 61432014 ; DP-140102164 ; JB149901) ; 61172146 ; LP-140100569) ; 61125106) |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000367253200004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/27739 |
| 专题 | 光谱成像技术研究室 |
| 通讯作者 | Yang, X |
| 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.,...&Yang, X.(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. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Shape-Constrained Sp（5085KB） | 期刊论文 | 作者接受稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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