Coronal Mass Ejections detection using multiple features based ensemble learning

	Coronal Mass Ejections detection using multiple features based ensemble learning
	Yin, Jianqin 1,2; Yao, Hai 2; Lin, Jiaben3 ; Yin, Yilong 4; Zhang, Ling 2; Liu, Xiaoli 2; Feng, Zhiquan 2; Wang, Xiaofan3
刊名	NEUROCOMPUTING
	2017-06-28
卷号	244 页码:123-130
关键词	Coronal Mass Ejections detection Multiple features fusion Ensemble learning
英文摘要	Coronal Mass Ejection (CME) is a major solar activity that affects the earth, thus CMEs detection is of great importance for space weather forecast, disaster prevention and reduction. We model the detection of Coronal Mass Ejections (CMEs) as the classification of the brightest block in the current running difference image. Because CMEs usually correspond to the areas with high gray values or complex texture features, multiple features including gray features and texture features are extracted to represent the brightest block. And classifier is designed based on these features. Our method includes four steps: first, because the CMEs spread along the radial direction of the sun, in order to facilitate the analysis, the original coordinate is transformed into the polar coordinate; Secondly, because the typical appearance of the CMEs is bright or complex texture enhancement, we use the brightest block to represent the whole image; Thirdly, we extract the gray, texture and HOG features of the brightest gray blocks. Finally, we use the extracted features to design decision trees as the base classifiers, and AdaBoost is used to obtain the final ensemble classifier. As far as we know, this is the first time that the learning based classification framework is presented in the CMEs detection. Moreover, multiple feature fusion is first used to model the various CMEs. Experimental results show that the integration of multi-feature based detection algorithm proposed can achieve better detection results. (C) 2017 Elsevier B.V. All rights reserved.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence
研究领域[WOS]	Computer Science
关键词[WOS]	AUTOMATIC DETECTION ; TRACKING ; CMES ; CLASSIFICATION ; CATALOG
收录类别	SCI
语种	英语
WOS记录号	WOS:000400040100012
内容类型	期刊论文
源URL	[http://ir.bao.ac.cn/handle/114a11/8857]
专题	国家天文台_太阳物理研究部
作者单位	1.Beijing Univ Posts & Telecommun, Sch Automat, Beijing 100876, Peoples R China 2.Univ Jinan, Sch Informat Sci & Engn, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Peoples R China 3.Chinese Acad Sci, Key Lab Solar Act, Beijing 100012, Peoples R China 4.Shandong Univ, Sch Comp Sci & Technol, Jinan 250002, Peoples R China
推荐引用方式 GB/T 7714	Yin, Jianqin,Yao, Hai,Lin, Jiaben,et al. Coronal Mass Ejections detection using multiple features based ensemble learning[J]. NEUROCOMPUTING,2017,244:123-130.
APA	Yin, Jianqin.,Yao, Hai.,Lin, Jiaben.,Yin, Yilong.,Zhang, Ling.,...&Wang, Xiaofan.(2017).Coronal Mass Ejections detection using multiple features based ensemble learning.NEUROCOMPUTING,244,123-130.
MLA	Yin, Jianqin,et al."Coronal Mass Ejections detection using multiple features based ensemble learning".NEUROCOMPUTING 244(2017):123-130.