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A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques
Wang, Pengyu1; Zhang, Yan1; Feng, Li2; Yuan, Hanqing1; Gan, Yuan1; Li, Shuting2,3; Lu, Lei2; Ying, Beili2,3; Gan, Weiqun2; Li, Hui2
2019-09-01
Source PublicationASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
ISSN0067-0049
Volume244Issue:1Pages:11
Corresponding AuthorZhang, Yan(zhangyannju@nju.edu.cn)
AbstractWith the accumulation of coronal mass ejection (CME) observations by coronagraphs, automatic detection and tracking of CMEs has proven to be crucial. The excellent performance of the convolutional neural network in image classification, object detection, and other computer vision tasks motivates us to apply it to CME detection and tracking as well. We developed a new tool for CME Automatic detection and tracking with MachinE Learning (CAMEL) techniques. The system is a three-module pipeline. It is first a supervised image classification problem. We solve it by training a neural network LeNet with training labels obtained from an existing CME catalog. Those images containing CME structures are flagged as CME images. Next, to identify the CME region in each CME-flagged image, we use deep descriptor transforming to localize the common object in an image set. A following step is to apply the graph cut technique to finely tune the detected CME region. To track the CME in an image sequence, the binary images with detected CME pixels are converted from a cartesian to a polar coordinate. A CME event is labeled if it can move in at least two frames and reach the edge of the coronagraph field of view. For each event, a few fundamental parameters are derived. The results of four representative CMEs with various characteristics are presented and compared with those from four existing automatic and manual catalogs. We find that CAMEL can detect more complete and weaker structures and has better performance to catch a CME as early as possible.
KeywordSun: coronal mass ejections (CMEs) techniques: image processing
DOI10.3847/1538-4365/ab340c
WOS KeywordCORONAL MASS EJECTIONS ; CATALOG
Indexed BySCI
Language英语
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000485673100001
PublisherIOP PUBLISHING LTD
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Document Type期刊论文
Identifierhttp://libir.pmo.ac.cn/handle/332002/27849
Collection中国科学院紫金山天文台
Corresponding AuthorZhang, Yan
Affiliation1.Nanjing Univ, Dept Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China
2.Chinese Acad Sci, Purple Mt Observ, Key Lab Dark Matter & Space Astron, Nanjing 210034, Jiangsu, Peoples R China
3.Univ Sci & Technol China, Sch Astron & Space Sci, Hefei 230026, Anhui, Peoples R China
Recommended Citation
GB/T 7714
Wang, Pengyu,Zhang, Yan,Feng, Li,et al. A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques[J]. ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,2019,244(1):11.
APA Wang, Pengyu.,Zhang, Yan.,Feng, Li.,Yuan, Hanqing.,Gan, Yuan.,...&Li, Hui.(2019).A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques.ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES,244(1),11.
MLA Wang, Pengyu,et al."A New Automatic Tool for CME Detection and Tracking with Machine-learning Techniques".ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 244.1(2019):11.
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