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Solar Active Regions Detection and Tracking Based on Deep Learning

文献类型：期刊论文

作者Gong, Long2; Yang, Yunfei2; Feng, Song2; Dai, Wei2; Liang, Bo2; Xiong JP(熊建萍)1
刊名SOLAR PHYSICS
出版日期2024-08
卷号299期号:8
关键词Active regions Multiobject detection Deep learning Target tracking
ISSN号0038-0938
DOI10.1007/s11207-024-02362-3
产权排序第2完成单位
文献子类Article
英文摘要Solar active regions serve as the primary energy sources of various solar activities, directly impacting the terrestrial environment. Therefore precise detection and tracking of active regions are crucial for space weather monitoring and forecasting. In this study, a total of 4577 HMI and MDI longitudinal magnetograms are selected for building the dataset, including the training set, validating set, and ten testing sets. They represent different observation instruments, different numbers of activity regions, and different time intervals. A new deep learning method, ReDetGraphTracker, is proposed for detecting and tracking the active regions in full-disk magnetograms. The cooperative modules, especially the redetection module, NSA Kalman filter, and the splitter module, better solve the problems of missing detection, discontinuous trajectory, drifting tracking bounding box, and ID change. The evaluation metrics IDF1, MOTA, MOTP, IDs, and FPS for the testing sets with 24-h interval on average are 74.0%, 74.7%, 0.130, 13.6, and 13.6, respectively. With the decreasing intervals, the metrics become better and better. The experimental results show that ReDetGraphTracker has a good performance in detecting and tracking active regions, especially capturing an active region as early as possible and terminating tracking in near-real time. It can well deal with the active regions whatever evolve drastically or with weak magnetic field strengths, in a near-real-time mode.
学科主题天文学 ; 太阳与太阳系
URL标识查看原文
出版地VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS
WOS关键词AUTOMATIC DETECTION
资助项目National Natural Science Foundation of China; SolarMonitor
WOS研究方向Astronomy & Astrophysics
语种英语
WOS记录号WOS:001302514200001
出版者SPRINGER
资助机构National Natural Science Foundation of China ; SolarMonitor
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27568]  
专题云南天文台_大样本恒星演化研究组
作者单位1.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650051, China
2.Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China;
推荐引用方式
GB/T 7714		 Gong, Long,Yang, Yunfei,Feng, Song,et al. Solar Active Regions Detection and Tracking Based on Deep Learning[J]. SOLAR PHYSICS,2024,299(8).
APA Gong, Long,Yang, Yunfei,Feng, Song,Dai, Wei,Liang, Bo,&熊建萍.(2024).Solar Active Regions Detection and Tracking Based on Deep Learning.SOLAR PHYSICS,299(8).
MLA Gong, Long,et al."Solar Active Regions Detection and Tracking Based on Deep Learning".SOLAR PHYSICS 299.8(2024).

入库方式： OAI收割

来源：云南天文台

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