| Solar Active Regions Detection and Tracking Based on Deep Learning | |
Gong, Long1; Yang, Yunfei1; Feng, Song1; Dai, Wei1; Liang, Bo1; Xiong JP(熊建萍)2
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| 发表期刊 | SOLAR PHYSICS
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| 2024-08 | |
| 卷号 | 299期号:8 |
| DOI | 10.1007/s11207-024-02362-3 |
| 产权排序 | 第2完成单位 |
| 收录类别 | SCI |
| 关键词 | Active regions Multiobject detection Deep learning Target tracking |
| 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. |
| 资助项目 | National Natural Science Foundation of China; SolarMonitor |
| 项目资助者 | National Natural Science Foundation of China ; SolarMonitor |
| 语种 | 英语 |
| 学科领域 | 天文学 ; 太阳与太阳系 |
| 文章类型 | Article |
| 出版者 | SPRINGER |
| 出版地 | VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS |
| ISSN | 0038-0938 |
| URL | 查看原文 |
| WOS记录号 | WOS:001302514200001 |
| WOS研究方向 | Astronomy & Astrophysics |
| WOS类目 | Astronomy & Astrophysics |
| 关键词[WOS] | AUTOMATIC DETECTION |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 版本 | 出版稿 |
| 条目标识符 | http://ir.ynao.ac.cn/handle/114a53/27568 |
| 专题 | 大样本恒星演化研究组 |
| 作者单位 | 1.Faculty of Information Engineering and Automation/Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming, 650500, Yunnan, China; 2.Yunnan Observatories, Chinese Academy of Sciences, Kunming, 650051, 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). |
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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 | ||
| Solar Active Regions（2798KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 | |
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