Classification of Spectrum Images of Solar Radio Bursts Based on Deep Learning

doi:10.1109/CIS.2019.00058

NSSC OpenIR

	Classification of Spectrum Images of Solar Radio Bursts Based on Deep Learning
Alternative Title	WOS:000557886100050
	Xu, Xirong 1,3; Wang, Zhiyang ; Hong, Yu ; Xia, Tianfan ; Cheng, Jun 1,2; Jiang, Jingqing 3
Source Publication	2019 15TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY (CIS 2019)
	2019
Pages	241-246
DOI	10.1109/CIS.2019.00058
Language	英语
ISBN	978-1-7281-6092-4
Abstract	Violent solar eruptions, such as solar flares and sundial jets, will have a serious impact on the Earth. Not only does it disrupt communication facilities and navigation equipment, but also produce a large number of radio phenomena. Further research on the solar radio phenomena will help us to understand of the activities of solar energy. With the rapid development of deep learning, the feature extraction and classification identification of solar radio spectrum images based on deep learning technology have been widely used. In this paper, gamma transformation, channel normalization, gray stretching, background suppression, image denoising and other image processing techniques, are used to preprocess the spectrum image of solar radio burst. Then we use CNN model, CNN-Capsule model and CNN-LSTM model to research the classification of spectrum images of solar radio burst types. The experimental results show that our approach can effectively improve the classification effect of solar radio burst types.
Keyword	solar radio Deep learning Image classification Digital image processing Capsule Networks CNN LSTM
Conference Name	15th International Conference on Computational Intelligence and Security (CIS)
Conference Date	DEC 13-16, 2019
Conference Place	Macao, PEOPLES R CHINA
Indexed By	CPCI
Citation statistics
Document Type	会议论文
Identifier	http://ir.nssc.ac.cn/handle/122/7766
Collection	中国科学院国家空间科学中心
Affiliation	1.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China 2.Natl Astron Observ, CAS Key Lab Solar Act, Beijing 100101, Peoples R China 3.Chinese Acad Sci, State Key Lab Space Weather, Beijing 100190, Peoples R China 4.Inner Mongolia Univ Nationalities, Sch Comp Sci & Technol, Tongliao, Inner Mongolia, Peoples R China
Recommended Citation GB/T 7714	Xu, Xirong,Wang, Zhiyang,Hong, Yu,et al. Classification of Spectrum Images of Solar Radio Bursts Based on Deep Learning[C],2019:241-246.

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