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Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning

文献类型：期刊论文

作者Dong, Qianqian2; Yang, Yunfei2; Feng, Song2; Dai, Wei2; Liang, Bo2; Xiong JP(熊建萍)1
刊名ASTROPHYSICAL JOURNAL
出版日期2024-08-01
卷号970期号:2
ISSN号0004-637X
DOI10.3847/1538-4357/ad4865
产权排序第2完成单位
文献子类Article
英文摘要China has six observing stations, providing over 52,000 handwritten sunspot drawings from 1947-2016. The observing stations are the Purple Mountain Astronomical Observatory (PMO), Yunnan Astronomical Observatory (YNAO), Qingdao Observatory Station (QDOS), Sheshan Observatory Station (SSOS), Beijing Planetarium (BJP), and Nanjing University (NJU). In this paper, we propose a new cotraining semisupervised learning method combining a semantic segmentation method named dynamic mutual training (DMT) boundary-guided semantic segmentation (BGSeg), i.e., DMT_BGSeg, which makes full use of the labeled data from PMO and the unlabeled data from the other five stations to detect and segment sunspot components in all sunspot drawings of the six Chinese stations. The sunspot is detected and segmented. Additionally, each sunspot is split into four types of components: pore, spot, umbra, and hole. The testing results show the mIoU values of PMO, YNAO, BJP, NJU, QDOS and SSOS are 85.29, 72.65, 73.82, 64.28, 62.26, and 60.07, respectively. The results of the comparison also show that DMT_BGSeg is effective in detecting and segmenting sunspots in Chinese sunspot drawings. The numbers and areas of sunspot components are measured separately. All of the detailed data are publicly shared on China-VO, which will advance the comprehensive augmentation of the global historical sunspot database and further the understanding of the long-term solar activity cycle and solar dynamo.
学科主题天文学 ; 太阳与太阳系
URL标识查看原文
出版地TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
WOS关键词ROTATION
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 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
WOS记录号WOS:001275376500001
出版者IOP Publishing Ltd
资助机构National Natural Science Foundation of China[11763004, 11573012, 11803085, 12063003] ; National Key Research and Development Program of China[2018YFA0404603] ; Yunnan Key Research and Development Program[2018IA054] ; Yunnan Applied Basic Research Project[2018FB103]
版本出版稿
源URL[http://ir.ynao.ac.cn/handle/114a53/27494]  
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, 396 YangFangWang, Guandu District, Kunming 650216, People's Republic of China; xiongjianping@nao.cas.cn
2.Faculty of Information Engineering and Automation, Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming 650500, People's Republic of China; dongqianqian@stu.kust.edu.cn, yangyf@kust.edu.cn, feng.song@kust.edu.cn, daiwei@kust.edu.cn, liangbo@kust.edu.cn;
推荐引用方式
GB/T 7714		 Dong, Qianqian,Yang, Yunfei,Feng, Song,et al. Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning[J]. ASTROPHYSICAL JOURNAL,2024,970(2).
APA Dong, Qianqian,Yang, Yunfei,Feng, Song,Dai, Wei,Liang, Bo,&熊建萍.(2024).Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning.ASTROPHYSICAL JOURNAL,970(2).
MLA Dong, Qianqian,et al."Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning".ASTROPHYSICAL JOURNAL 970.2(2024).

入库方式： OAI收割

来源：云南天文台

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