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Automatic classification of mesoscale auroral forms using convolutional neural networks
Guo, Z.-X.1,2; Yang, J.-Y.1,2,3; Dunlop, M. W.1,2,4; Cao, J.-B.1,2; Li, L.-Y.1,2; Ma, Y.-D.1,2; Ji KF(季凯帆)5; Xiong, C.6; Li, J.1,2; Ding, W.-T.7
发表期刊Journal of Atmospheric and Solar-Terrestrial Physics
2022-09-01
卷号235
DOI10.1016/j.jastp.2022.105906
产权排序第5完成单位
收录类别SCI ; EI
摘要

Convolutional neural networks (CNNs) in deep learning enable the extraction of features in image data. Through the multi-layer superposition of a convolutional neural network, we can better capture the essential characteristics of different auroral subclasses and further classify auroral images in detail. Because the auroral morphological features often present abstract characteristics, our study compares different CNN architectures and different layering in order to test the best neural network model for mesoscale aurora classification. Although the classification models and subclasses used by us are both more complex, the highest F1 score of aurora classification of the test set reaches 99.6% (ResNet-50), which performs best comparing with previous works. Our classification models work also quite well when applied to an independent auroral image sequence, declaring our approach can automatically select images of various mesoscale auroral forms using CNNs, and allow the time sequence of auroral evolution to be seen automatically through the mesoscale auroral feature recognitions. 

资助项目National Natural Science Foundation of China (NSFC)[41821003] ; National Natural Science Foundation of China (NSFC)[41874193] ; National Natural Science Foundation of China (NSFC)[41431071] ; NERC Highlight Topic SWIGS[NE/P016863/1] ; STFC[ST/M001083/1] ; STFC
项目资助者National Natural Science Foundation of China (NSFC)[41821003, 41874193, 41431071] ; NERC Highlight Topic SWIGS[NE/P016863/1] ; STFC[ST/M001083/1] ; STFC
语种英语
学科领域计算机科学技术 ; 人工智能 ; 计算机应用
学科门类工学 ; 工学::计算机科学与技术(可授工学、理学学位)
文章类型Article
出版者PERGAMON-ELSEVIER SCIENCE LTD
出版地THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND
ISSN1364-6826
URL查看原文
WOS记录号WOS:000809900600006
WOS研究方向Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences
WOS类目Geochemistry & Geophysics ; Meteorology & Atmospheric Sciences
关键词[WOS]POLEWARD BOUNDARY ; INTENSIFICATIONS
EI入藏号20222212185653
EI主题词Convolutional neural networks
EI分类号461.4 Ergonomics and Human Factors Engineering - 716.1 Information Theory and Signal Processing - 903.1 Information Sources and Analysis
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ynao.ac.cn/handle/114a53/25212
专题天文技术实验室
通讯作者Yang, J.-Y.
作者单位1.Space Science Institute, School of Space and Environment, Beihang University, Beijing, 100191, China;
2.Key Laboratory of Space Environment Monitoring and Information Processing, Ministry of Industry and Information Technology, China;
3.State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing, China;
4.RAL_Space, STFC, Chilton, Oxfordshire, OX11 0QX, United Kingdom;
5.Yunnan Observatory of Chinese Academy of Science, Yunnan, 650216, China;
6.Department of Space Physics, Electronic Information School, Wuhan University, Wuhan, 430072, China;
7.Sinosteel Tendering Co., LTD, No. 8, Haidian Street, Haidian District, Beijing, China
推荐引用方式
GB/T 7714
Guo, Z.-X.,Yang, J.-Y.,Dunlop, M. W.,et al. Automatic classification of mesoscale auroral forms using convolutional neural networks[J]. Journal of Atmospheric and Solar-Terrestrial Physics,2022,235.
APA Guo, Z.-X..,Yang, J.-Y..,Dunlop, M. W..,Cao, J.-B..,Li, L.-Y..,...&Ding, W.-T..(2022).Automatic classification of mesoscale auroral forms using convolutional neural networks.Journal of Atmospheric and Solar-Terrestrial Physics,235.
MLA Guo, Z.-X.,et al."Automatic classification of mesoscale auroral forms using convolutional neural networks".Journal of Atmospheric and Solar-Terrestrial Physics 235(2022).
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