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Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity
Zhang, Ling1; Yin, Jian-qin2,3; Lin, Jia-ben3; Feng, Zhi-quan1; Zhou, Jin1
2017-07-01
Source PublicationSOLAR PHYSICS
Volume292Issue:7
AbstractCoronal Mass Ejections (CMEs) release tremendous amounts of energy in the solar system, which has an impact on satellites, power facilities and wireless transmission. To effectively detect a CME in Large Angle Spectrometric Coronagraph (LASCO) C2 images, we propose a novel algorithm to locate the suspected CME regions, using the Extreme Learning Machine (ELM) method and taking into account the features of the grayscale and the texture. Furthermore, space-time continuity is used in the detection algorithm to exclude the false CME regions. The algorithm includes three steps: i) define the feature vector which contains textural and grayscale features of a running difference image; ii) design the detection algorithm based on the ELM method according to the feature vector; iii) improve the detection accuracy rate by using the decision rule of the space-time continuum. Experimental results show the efficiency and the superiority of the proposed algorithm in the detection of CMEs compared with other traditional methods. In addition, our algorithm is insensitive to most noise.
SubtypeArticle
KeywordCoronal Mass Ejections Initiation And Propagation Corona Models
WOS HeadingsScience & Technology ; Physical Sciences
Funding OrganizationNational Natural Science Foundation of China(61203341 ; National Natural Science Foundation of China(61203341 ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Foundation of University of Jinan(XKY1513) ; Foundation of University of Jinan(XKY1513) ; 61375084 ; 61375084 ; 61640218 ; 61640218 ; 61472163 ; 61472163 ; 61673192) ; 61673192) ; National Natural Science Foundation of China(61203341 ; National Natural Science Foundation of China(61203341 ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Foundation of University of Jinan(XKY1513) ; Foundation of University of Jinan(XKY1513) ; 61375084 ; 61375084 ; 61640218 ; 61640218 ; 61472163 ; 61472163 ; 61673192) ; 61673192)
DOI10.1007/s11207-017-1107-2
WOS KeywordEXTREME LEARNING-MACHINE ; AUTOMATIC DETECTION ; TRACKING ; CLASSIFICATION ; IMAGES ; EARTH
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61203341 ; National Natural Science Foundation of China(61203341 ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Foundation of University of Jinan(XKY1513) ; Foundation of University of Jinan(XKY1513) ; 61375084 ; 61375084 ; 61640218 ; 61640218 ; 61472163 ; 61472163 ; 61673192) ; 61673192) ; National Natural Science Foundation of China(61203341 ; National Natural Science Foundation of China(61203341 ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Fund for Outstanding Youth of Shandong Provincial High School(ZR2016JL023) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Project of Shandong Province Higher Educational Science and Technology Program(J16LN07) ; Foundation of University of Jinan(XKY1513) ; Foundation of University of Jinan(XKY1513) ; 61375084 ; 61375084 ; 61640218 ; 61640218 ; 61472163 ; 61472163 ; 61673192) ; 61673192)
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000406304300009
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/8882
Collection太阳物理研究部
Affiliation1.Univ Jinan, Sch Informat Sci & Engn, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Shandong, Peoples R China
2.Beijing Univ Posts & Telecommun, Automat Sch, Beijing 100876, Peoples R China
3.Chinese Acad Sci, Key Lab Solar Act, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Ling,Yin, Jian-qin,Lin, Jia-ben,et al. Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity[J]. SOLAR PHYSICS,2017,292(7).
APA Zhang, Ling,Yin, Jian-qin,Lin, Jia-ben,Feng, Zhi-quan,&Zhou, Jin.(2017).Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity.SOLAR PHYSICS,292(7).
MLA Zhang, Ling,et al."Detection of Coronal Mass Ejections Using Multiple Features and Space-Time Continuity".SOLAR PHYSICS 292.7(2017).
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