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HINet： A Multi-source Data Fusion Network for Hail Identification
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Xiaowen ZHANG1, 2, Peiwen YU3, Jian SHANG4, 5, 6, Shan HUA1, 2, Qishao ZHANG2
Journal of Telemetry, Tracking and Command | 2024, 45(4) : 45 - 56
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Journal of Telemetry, Tracking and Command | 2024, 45(4): 45-56
• Artificial Intelligence Technology •
HINet： A Multi-source Data Fusion Network for Hail Identification
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Xiaowen ZHANG1, 2, Peiwen YU3, Jian SHANG4, 5, 6, Shan HUA1, 2, Qishao ZHANG2
Affiliations
  • 1.National Meteorological Center, Beijing 100081, China
  • 2.Anyang National Climatological Observatory, Anyang 455000, China
  • 3.School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China
  • 4.National Satellite Meteorological Center (National Centre for Space Weather), Beijing 100081, China
  • 5.Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China
  • 6.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, Beijing 100081, China
Published: 2024-07-15 doi: 10.12347/j.ycyk.20240520002
Outline
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Hailstorms are characterized by their suddenness, localized nature and high destructive power. Although observations acquired by ground-based automatic stations, radars and satellites play a certain role in hail identification, the limitation of single observation data leads to a high false alarm rate and low accuracy rate in hail identification. Therefore, there is an urgent need to construct a hail identification technology based on multisource high-resolution observation. In this paper, a multi-source data fusion network for hail recognition is proposed. The deep learning method utilizes the spatio-temporal feature extraction module, the multi-source data feature fusion module, and the UCUNet (U Connection Unet) recognition module to fully exploit the spatio-temporal features of the multi-source data such as FY4B (FengYun-4B star) satellites, weather radar, and numerical models when hail occurs, and innovatively adds the topographic height, slope, and slope direction as hail recognition factors. In order to evaluate the performance of the proposed network method, this paper conducts a series of experiments and compares the experimental results with real labeled data. The results show that HINet (Hail Identification Net) can make full use of multi-source data and effectively improve the hail identification results under complex terrain conditions. The network model proposed in this paper has high accuracy and practicality in hail research and identification.

Hail identification  /  Deep learning  /  Spatio-temporal feature extraction  /  Multi-source data feature fusion  /  Complex terrain
Xiaowen ZHANG, Peiwen YU, Jian SHANG, Shan HUA, Qishao ZHANG. HINet： A Multi-source Data Fusion Network for Hail Identification[J]. Journal of Telemetry, Tracking and Command, 2024 , 45 (4) : 45 -56 . DOI: 10.12347/j.ycyk.20240520002
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Year 2024 volume 45 Issue 4
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Article Info
doi: 10.12347/j.ycyk.20240520002
  • Receive Date：2024-05-20
  • Online Date：2026-03-20
  • Published：2024-07-15
Article Data
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History
  • Received：2024-05-20
  • Revised：2024-06-17
Funding
Affiliations
    1.National Meteorological Center, Beijing 100081, China
    2.Anyang National Climatological Observatory, Anyang 455000, China
    3.School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China
    4.National Satellite Meteorological Center (National Centre for Space Weather), Beijing 100081, China
    5.Innovation Center for FengYun Meteorological Satellite (FYSIC), Beijing 100081, China
    6.Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites, Beijing 100081, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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