Affiliation:
1. State Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing China
2. Hainan National Field Science Observation and Research Observatory for Space Weather Danzhou, Hainan Province China
3. State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China
4. University of Chinese Academy of Sciences Beijing China
Abstract
AbstractAn 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.
Funder
National Key Research and Development Program of China
Publisher
American Geophysical Union (AGU)
Cited by
1 articles.
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