NAOC Open IR
Radio frequency interference detection based on the AC-UNet model
Yan, Rui-Qing1; Dai, Cong1; Liu, Wei2; Li, Ji-Xia3; Chen, Si-Ying1; Yu, Xian-Chuan1; Zuo, Shi-Fan3; Chen, Xue-Lei3
2021-06-01
Source PublicationRESEARCH IN ASTRONOMY AND ASTROPHYSICS
ISSN1674-4527
Volume21Issue:5Pages:11
AbstractRadio frequency interference (RFI) is a serious issue in radio astronomy. This paper proposes a U-Net network model with atrous convolution to detect RFI. Using the ability of convolutional neural networks to extract image features of RFI, and learning RFI distribution patterns, the detection model of the RFI is established. We use observational data containing real RFIs obtained by the Tianlai telescope to train the model so that the model can detect RFI. Calculate the probability of a data point being RFI pixel by pixel, and set a threshold. At the same time the dropout layer was added to avoid overfitting problems. If the predicted probability of a data point exceeds the threshold, it is considered that there is RFI, and if the predicted probability of a data point does not exceed the threshold, then it is considered that there is no RFI, so that the part of the image with RFI is flagged. Experimental results show that this approach can achieve satisfactory accuracy in the detection of radio observation images with a small amount of RFI.
Keywordmethods data analysis techniques image processing methods observational
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University
DOI10.1088/1674-4527/21/5/119
WOS KeywordNEURAL-NETWORKS
Language英语
Funding ProjectNational Natural Science Foundation of China[11471045] ; National Natural Science Foundation of China[41672323] ; Interdiscipline Research Funds of Beijing Normal University
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; Interdiscipline Research Funds of Beijing Normal University ; Interdiscipline Research Funds of Beijing Normal University
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS IDWOS:000663182700001
PublisherNATL ASTRONOMICAL OBSERVATORIES, CHIN ACAD SCIENCES
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/77454
Collection中国科学院国家天文台
Corresponding AuthorYu, Xian-Chuan
Affiliation1.Beijing Normal Univ, Coll Informat Sci & Technol, Beijing 100875, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Natl Astron Observ, Beijing 100101, Peoples R China
Recommended Citation
GB/T 7714
Yan, Rui-Qing,Dai, Cong,Liu, Wei,et al. Radio frequency interference detection based on the AC-UNet model[J]. RESEARCH IN ASTRONOMY AND ASTROPHYSICS,2021,21(5):11.
APA Yan, Rui-Qing.,Dai, Cong.,Liu, Wei.,Li, Ji-Xia.,Chen, Si-Ying.,...&Chen, Xue-Lei.(2021).Radio frequency interference detection based on the AC-UNet model.RESEARCH IN ASTRONOMY AND ASTROPHYSICS,21(5),11.
MLA Yan, Rui-Qing,et al."Radio frequency interference detection based on the AC-UNet model".RESEARCH IN ASTRONOMY AND ASTROPHYSICS 21.5(2021):11.
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