Volume 41 Issue 4
Jul.  2021
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Article Navigation >  Chinese Journal of Space Science  > 2021  >  41(4): 667-675
WANG Zihan, TONG Jizhou, ZOU Ziming, ZHONG Jia, BAI Xi. Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation[J]. Chinese Journal of Space Science, 2021, 41(4): 667-675. doi: 10.11728/cjss2021.04.667
Citation: WANG Zihan, TONG Jizhou, ZOU Ziming, ZHONG Jia, BAI Xi. Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation[J]. Chinese Journal of Space Science, 2021, 41(4): 667-675. doi: 10.11728/cjss2021.04.667
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Auroral Oval Morphology Extraction Based on U-net from Ultraviolet Aurora Observation

doi: 10.11728/cjss2021.04.667 cstr: 32142.14.cjss2021.04.667

  • 1. National Space Science Center, Chinese Academy of Sciences, Beijing 100190;

  • 2. University of Chinese Academy of Sciences, Beijing 100049

  • Received Date: 2020-02-11
  • Rev Recd Date: 2020-12-27
  • Publish Date: 2021-07-15
    Abstract
  • Auroral oval morphology extraction plays an important role in the aurora research. How to improve the accuracy of auroral oval morphology extraction in ultraviolet aurora images with strong interference background is still an incomplete problem. In this paper, a method based on deep learning semantic segmentation model U-net is proposed. U-net model with residual block is used to extract auroral oval morphology with high accuracy. The experimental results on Polar satellite ultraviolet aurora images show that this method can get higher accuracy compared with the existing algorithms, and can obtain more detailed extraction results for both full auroral oval and gap auroral oval images. This method shows its advantages especially for aurora images with strong dayglow interference, uneven grayscale and low contrast. At the same time, the applicability and effectiveness of supervised deep learning method on auroral oval morphology extraction have been proved.

     

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