Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning

doi:10.3847/1538-4357/ad4865

YNAO OpenIR > 大样本恒星演化研究组

	Extraction of Sunspots from Chinese Sunspot Drawings Based on Semisupervised Learning
	Dong, Qianqian 1; Yang, Yunfei 1; Feng, Song 1; Dai, Wei 1; Liang, Bo 1; Xiong JP(熊建萍)2
发表期刊	ASTROPHYSICAL JOURNAL
	2024-08-01
卷号	970 期号:2
DOI	10.3847/1538-4357/ad4865
产权排序	第2完成单位
收录类别	SCI
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.
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]
项目资助者	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]
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	IOP Publishing Ltd
出版地	TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND
ISSN	0004-637X
URL	查看原文
WOS记录号	WOS:001275376500001
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
关键词[WOS]	ROTATION
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/114a53/27494
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, People's Republic of China; [email protected], [email protected], [email protected], [email protected], [email protected]; 2.Yunnan Observatories, Chinese Academy of Sciences, 396 YangFangWang, Guandu District, Kunming 650216, People's Republic of China; [email protected]
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).

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