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Dense-FCN: A Deep Learning Approach for Weather Radar Beam Blockage Correction

Sun, Jiawei; Wu, Hao; Liu, Qi; Liu, Xiaodong; Ma, Jianhao

Authors

Jiawei Sun

Hao Wu

Qi Liu

Jianhao Ma



Abstract

The weather radar will receive a lot of non-meteorological echo information during the body scan process, such as: object echoes, co-wave interference echoes, airplanes, flocks of birds, etc. These non-meteorological echoes will cause pollution to normal meteorological echoes, thereby affecting the accuracy of the output product. The traditional weather radar beam blocking correction method is to interpolate and fill according to the adjacent radial data, but this method cannot make full use of the deep laws of radar data and is relatively limited. In this paper, the weather radar beam blocking correction problem is regarded as an image completion problem, a fully convolutional neural network with dense connection is designed, and the multiclass cross entropy loss function is used to optimize the model. The model proposed in this paper has achieved good performance in the evaluation indicators in the field of meteorology and image completion.

Presentation Conference Type Conference Paper (Published)
Conference Name 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
Start Date Sep 12, 2022
End Date Sep 15, 2022
Acceptance Date Jul 25, 2022
Online Publication Date Dec 13, 2022
Publication Date 2022
Deposit Date Jan 27, 2023
Publisher Institute of Electrical and Electronics Engineers
Book Title 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)
DOI https://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927851