Volume 43 Issue 2
Mar.  2023
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Article Navigation >  Chinese Journal of Space Science  > 2023  >  43(2): 231-240 Open Access
XIA Kai, XING Zanyang, ZHANG Qinghe, WANG Yanling, YANG Qiuju, LU Sheng, LIU Zhenping. Automatic Identification of Space Hurricane Based on Transfer Learning (in Chinese). Chinese Journal of Space Science, 2023, 43(2): 231-240 doi: 10.11728/cjss2023.02.2022-0031
Citation: XIA Kai, XING Zanyang, ZHANG Qinghe, WANG Yanling, YANG Qiuju, LU Sheng, LIU Zhenping. Automatic Identification of Space Hurricane Based on Transfer Learning (in Chinese). Chinese Journal of Space Science, 2023, 43(2): 231-240 doi: 10.11728/cjss2023.02.2022-0031
shu

Automatic Identification of Space Hurricane Based on Transfer Learning

doi: 10.11728/cjss2023.02.2022-0031 cstr: 32142.14.cjss2023.02.2022-0031
  • 1.

    School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209

  • 2.

    Shandong Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong University, Weihai 264209

  • 3.

    School of Physics and Information Technology, Shaanxi Normal University, Xi’an 710062

  • Received Date: 2022-07-11
  • Accepted Date: 2022-11-21
  • Rev Recd Date: 2022-10-22
    • Available Online: 2022-11-28
    Abstract
  • Space hurricane is a newly discovered large-scale and bright spot-like auroral structure in the polar cap region, which visually characterizes a solar wind energy injection phenomenon comparable to a magnetic storm during the geomagnetic calm period, this updates the understanding of the solar wind-magnetosphere-ionosphere coupling process, it is of great scientific importance to accurately and efficiently identify space hurricane events from the huge amount of auroral data. Deep learning is used, six networks are compared, and an automatic space hurricane identification method based on Transfer learning and EfficientNetB2 is proposed, validate the effectiveness of the model in DMSP/SSUSI observations from 2005 to 2021 with an accuracy of 97.7%. The results show that the method can be used to automatically identify Space hurricane events from a large amount of satellite-based auroral observation data.

     

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