Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method
Abstract An intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a un...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-11-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003498 |
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| _version_ | 1841536481336754176 |
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| author | Zheng Wang Meiyi Zhan Pengdong Gao Guojun Wang Chu Qiu Quan Qi Jiankui Shi Xiao Wang |
| author_facet | Zheng Wang Meiyi Zhan Pengdong Gao Guojun Wang Chu Qiu Quan Qi Jiankui Shi Xiao Wang |
| author_sort | Zheng Wang |
| collection | DOAJ |
| description | Abstract An intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification. |
| format | Article |
| id | doaj-art-504bcdf0f1cf4c9cbf66f02d2cab026e |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-504bcdf0f1cf4c9cbf66f02d2cab026e2025-01-14T16:26:48ZengWileySpace Weather1542-73902023-11-012111n/an/a10.1029/2023SW003498Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning MethodZheng Wang0Meiyi Zhan1Pengdong Gao2Guojun Wang3Chu Qiu4Quan Qi5Jiankui Shi6Xiao Wang7State Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Media Convergence and Communication Communication University of China Beijing ChinaState Key Laboratory of Media Convergence and Communication Communication University of China Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Media Convergence and Communication Communication University of China Beijing ChinaState Key Laboratory of Media Convergence and Communication Communication University of China Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaAbstract An intelligent Spread‐F image detection and classification method is presented in this paper based on an ionogram image set using deep learning models. The ionogram images from the Hainan station, spanning from 2002 to 2015, have been manually labeled into five categories, resulting in a unique ionogram image set for supervised learning models. To balance the number of different types, simulated noises were added to these images. Based on 80,000 samples with Spread‐F and 20,000 samples without, numerous experiments have been conducted to train VGG, ResNet, EfficientNet, ViT, MobileNet, and other networks. The results on the test set indicate that these models except VGG have a good ability of exacting features of different types, leading to a high level of accuracy in detecting Spread‐F and a relatively accurate classification of it. The ionogram images in 2016 are then employed as another test set to further examine the performance of the trained models. Both quantitative and qualitative analyses have demonstrated the results obtained by deep learning models are highly consistent with manual identification.https://doi.org/10.1029/2023SW003498Spread‐Flow latitude ionosphereionogram image setdeep learningintelligent classification |
| spellingShingle | Zheng Wang Meiyi Zhan Pengdong Gao Guojun Wang Chu Qiu Quan Qi Jiankui Shi Xiao Wang Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method Space Weather Spread‐F low latitude ionosphere ionogram image set deep learning intelligent classification |
| title | Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method |
| title_full | Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method |
| title_fullStr | Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method |
| title_full_unstemmed | Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method |
| title_short | Automatic Detection and Classification of Spread‐F From Ionosonde at Hainan With Image‐Based Deep Learning Method |
| title_sort | automatic detection and classification of spread f from ionosonde at hainan with image based deep learning method |
| topic | Spread‐F low latitude ionosphere ionogram image set deep learning intelligent classification |
| url | https://doi.org/10.1029/2023SW003498 |
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