Deep Active Learning–Based Classification of Solar Radio Spectrogram Data
The study of solar burst activity can provide early warnings for the environmental protection of the solar–terrestrial space environment. With the improvement of solar radio observation techniques, observation devices have generated enormous amounts of observation data. To solve the shortcomings of...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal Supplement Series |
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| Online Access: | https://doi.org/10.3847/1538-4365/adda30 |
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| _version_ | 1850087247281389568 |
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| author | Yan Liu HongQiang Song FaBao Yan YanRui Su |
| author_facet | Yan Liu HongQiang Song FaBao Yan YanRui Su |
| author_sort | Yan Liu |
| collection | DOAJ |
| description | The study of solar burst activity can provide early warnings for the environmental protection of the solar–terrestrial space environment. With the improvement of solar radio observation techniques, observation devices have generated enormous amounts of observation data. To solve the shortcomings of time-consuming and error-prone manual recognition, researchers have begun to use deep learning to recognize and automatically classify solar radio outbursts. Deep learning will depend on a large number of labeled samples; however, the labeling of samples requires a lot of time and manual labor. This leads to low efficiency. In addition, the labeled samples are not all valuable samples, so it is necessary to improve the effectiveness of the labeled samples and select the high-value samples. The occurrence of active-learning techniques provides an opportunity to solve this problem. In this study, we developed a progressive deep convolutional generative adversarial network model. Then, we combined it with deep active learning to complete the automatic classification of solar radio spectrum data. We used solar radio spectrum data from the Chashan Observatory (CSO) of Shandong University and Learmonth Observatory in Australia. The results show that the method proposed in this paper can achieve high accuracy in the automatic recognition of solar radio spectrum data and solve the time-consuming problem of labeling a huge number of data samples. Finally, we applied the results to the CSO and realized the automatic recognition of solar radio spectral data. |
| format | Article |
| id | doaj-art-a9ae32ca33e5453f84c826571532bb6c |
| institution | DOAJ |
| issn | 0067-0049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Supplement Series |
| spelling | doaj-art-a9ae32ca33e5453f84c826571532bb6c2025-08-20T02:43:15ZengIOP PublishingThe Astrophysical Journal Supplement Series0067-00492025-01-0127912510.3847/1538-4365/adda30Deep Active Learning–Based Classification of Solar Radio Spectrogram DataYan Liu0HongQiang Song1https://orcid.org/0000-0001-5705-661XFaBao Yan2https://orcid.org/0000-0002-4451-7293YanRui Su3Laboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University , Weihai 264209, People’s Republic of China ; suyanrui@sdu.edu.cnLaboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University , Weihai 264209, People’s Republic of China ; suyanrui@sdu.edu.cnLaboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University , Weihai 264209, People’s Republic of China ; suyanrui@sdu.edu.cn; School of Mechanical, Electrical & Information Engineering, Shandong University , Weihai 264209, People’s Republic of China; Shandong Key Laboratory of Intelligent Electronic Packaging, Testing and Application, Shandong University , Weihai 264209, People’s Republic of ChinaLaboratory for ElectromAgnetic Detection (LEAD), Institute of Space Sciences, Shandong University , Weihai 264209, People’s Republic of China ; suyanrui@sdu.edu.cn; School of Mechanical, Electrical & Information Engineering, Shandong University , Weihai 264209, People’s Republic of China; Shandong Key Laboratory of Intelligent Electronic Packaging, Testing and Application, Shandong University , Weihai 264209, People’s Republic of ChinaThe study of solar burst activity can provide early warnings for the environmental protection of the solar–terrestrial space environment. With the improvement of solar radio observation techniques, observation devices have generated enormous amounts of observation data. To solve the shortcomings of time-consuming and error-prone manual recognition, researchers have begun to use deep learning to recognize and automatically classify solar radio outbursts. Deep learning will depend on a large number of labeled samples; however, the labeling of samples requires a lot of time and manual labor. This leads to low efficiency. In addition, the labeled samples are not all valuable samples, so it is necessary to improve the effectiveness of the labeled samples and select the high-value samples. The occurrence of active-learning techniques provides an opportunity to solve this problem. In this study, we developed a progressive deep convolutional generative adversarial network model. Then, we combined it with deep active learning to complete the automatic classification of solar radio spectrum data. We used solar radio spectrum data from the Chashan Observatory (CSO) of Shandong University and Learmonth Observatory in Australia. The results show that the method proposed in this paper can achieve high accuracy in the automatic recognition of solar radio spectrum data and solve the time-consuming problem of labeling a huge number of data samples. Finally, we applied the results to the CSO and realized the automatic recognition of solar radio spectral data.https://doi.org/10.3847/1538-4365/adda30Solar radio telescopes |
| spellingShingle | Yan Liu HongQiang Song FaBao Yan YanRui Su Deep Active Learning–Based Classification of Solar Radio Spectrogram Data The Astrophysical Journal Supplement Series Solar radio telescopes |
| title | Deep Active Learning–Based Classification of Solar Radio Spectrogram Data |
| title_full | Deep Active Learning–Based Classification of Solar Radio Spectrogram Data |
| title_fullStr | Deep Active Learning–Based Classification of Solar Radio Spectrogram Data |
| title_full_unstemmed | Deep Active Learning–Based Classification of Solar Radio Spectrogram Data |
| title_short | Deep Active Learning–Based Classification of Solar Radio Spectrogram Data |
| title_sort | deep active learning based classification of solar radio spectrogram data |
| topic | Solar radio telescopes |
| url | https://doi.org/10.3847/1538-4365/adda30 |
| work_keys_str_mv | AT yanliu deepactivelearningbasedclassificationofsolarradiospectrogramdata AT hongqiangsong deepactivelearningbasedclassificationofsolarradiospectrogramdata AT fabaoyan deepactivelearningbasedclassificationofsolarradiospectrogramdata AT yanruisu deepactivelearningbasedclassificationofsolarradiospectrogramdata |