Solar-Filament Detection and Classification Based on Deep Learning
文献类型：期刊论文
| 作者 | Guo, Xulong1; Yang, Yunfei1; Feng, Song1; Bai, Xianyong2; Liang, Bo1; Dai, Wei1 |
| 刊名 | SOLAR PHYSICS
 |
| 出版日期 | 2022-08-01 |
| 卷号 | 297期号:8页码:19 |
| ISSN号 | 0038-0938 |
| 关键词 | Solar filament Image segmentation Fragments Deep learning |
| DOI | 10.1007/s11207-022-02019-z |
| 英文摘要 | Solar filaments are distinct strip-like structures observed in chromospheric He z images. Filament eruptions, flares, and coronal mass ejections (CMEs) can be regarded as the same physical process of releasing magnetic energy at different times and solar atmosphere heights. It is very important to detect filaments for forecasting flares and CMEs. This article proposes a new solar-filament detection and classification method based on Condlnst; a deep-learning model. A data set of solar filaments is built, including ten thousand filaments. To distinguish filaments that consist of only a single connected dark region and filaments that are broken into several fragments, the filaments are classified into isolated filaments and non-isolated filaments. The mean precision, recall, AP, and F1 obtained using the proposed method are 90.83%, 83.88%, 82.86%, and 87.22%, respectively. The results show that the method performs well in detecting and classifying isolated and non-isolated filaments, especially in solving the fragments problem of how to detect a filament that is broken into several fragments. The method also has good performance in handling various images, even with existing uneven brightness or low contrast. The precision of filament masks still needs to be improved in the future. |
| WOS关键词 | RECOGNITION ; ERUPTIONS |
| 资助项目 | National Natural Science Foundation of China[11763004] ; National Natural Science Foundation of China[11573012] ; National Natural Science Foundation of China[11803085] ; National Natural Science Foundation of China[12063003] ; National Natural Science Foundation of China[U1931107] ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences[KLSA202019] ; National Key Research and Development Program of China[2018YFA0404603] ; Yunnan Key Research and Development Program[2018IA054] ; Yunnan Applied Basic Research Project[2018FB103] |
| WOS研究方向 | Astronomy & Astrophysics |
| 语种 | 英语 |
| 出版者 | SPRINGER |
| WOS记录号 | WOS:000842188900002 |
| 资助机构 | National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Yunnan Key Research and Development Program ; Yunnan Key Research and Development Program ; Yunnan Applied Basic Research Project ; Yunnan Applied Basic Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Yunnan Key Research and Development Program ; Yunnan Key Research and Development Program ; Yunnan Applied Basic Research Project ; Yunnan Applied Basic Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Yunnan Key Research and Development Program ; Yunnan Key Research and Development Program ; Yunnan Applied Basic Research Project ; Yunnan Applied Basic Research Project ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; Open Research Program of the Key Laboratory of Solar Activity of the Chinese Academy of Sciences ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; Yunnan Key Research and Development Program ; Yunnan Key Research and Development Program ; Yunnan Applied Basic Research Project ; Yunnan Applied Basic Research Project |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/87707]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Yang, Yunfei |
| 作者单位 | 1.Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Yunnan Key Lab Comp Technol Applicat, Kunming 650500, Yunnan, Peoples R China 2.Natl Astron Observ, CAS Key Lab Solar Act, Beijing 100012, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guo, Xulong,Yang, Yunfei,Feng, Song,et al. Solar-Filament Detection and Classification Based on Deep Learning[J]. SOLAR PHYSICS,2022,297(8):19. |
| APA | Guo, Xulong,Yang, Yunfei,Feng, Song,Bai, Xianyong,Liang, Bo,&Dai, Wei.(2022).Solar-Filament Detection and Classification Based on Deep Learning.SOLAR PHYSICS,297(8),19. |
| MLA | Guo, Xulong,et al."Solar-Filament Detection and Classification Based on Deep Learning".SOLAR PHYSICS 297.8(2022):19. |
入库方式： OAI收割
来源：国家天文台
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