Radar image prediction using generative adversarial networks

Lei Han; Liyuan Fang; Wei Zhang; Yurong Ge

doi:10.1117/12.2589379

27 January 2021 Radar image prediction using generative adversarial networks

Lei Han, Liyuan Fang, Wei Zhang, Yurong Ge

Proceedings Volume 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020); 1172020 (2021) https://doi.org/10.1117/12.2589379
Event: Twelfth International Conference on Graphics and Image Processing, 2020, Xi'an, China

Abstract

Doppler radar is the main remote sensing equipment to monitor severe convective weather which has significant threats to social and economic activities. It is important to accurately predict the time and location of severe weather events. In this study, we use a deep learning technique to predict severe weather events based on radar images. Firstly, we transform the prediction problem into a binary classification problem and use Generative Adversarial Networks (GANs) to construct a classifier. Then Doppler radar images are used to train the model. The critical success index, probability of detection, and false alarm ratio are used to evaluate the prediction results. The experimental results show that the GANs model provides satisfactory results.

Citation Download Citation

Lei Han, Liyuan Fang, Wei Zhang, and Yurong Ge "Radar image prediction using generative adversarial networks", Proc. SPIE 11720, Twelfth International Conference on Graphics and Image Processing (ICGIP 2020), 1172020 (27 January 2021); https://doi.org/10.1117/12.2589379

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