Deep feature extraction and its application for hailstorm detection in a large collection of radar images

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With the improvement of sensing and storing technologies, a large amount of weather data become available, and the data size will continue growing as radar imaging instruments continuously acquire data. In this work, we develop a deep convolutional neural network with a large collection of radar images as input to train and validate a classification model, and then we use the model to detect hailstorm events. This is interdisciplinary work between the disciplines of computer science and meteorology. We are primarily interested in what hailstorm features the network learns and how it learns as convolving into deeper iterations. The evaluation results show a high classification accuracy in comparison with existing hailstorm detection approaches. The proposed approach can also be used to detect other types of severe weather events with minimal efforts on variable or parameter changes.

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This research work was supported by NASA Grant NNM11AA01A. We thank Dr. Sundar A. Christopher, Professor of Atmospheric Science at UAH, for his insightful suggestions for this work. We thank Ms. Melinda Pullman who helped us organize the data from National Center for Environmental Information Storm Events Database. We thank the support of Department of Computer Science at UAH and the support of NASA.

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Correspondence to Chao Peng.

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This research was supported by NASA Grant NNM11AA01A.

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Gurung, I., Peng, C., Maskey, M. et al. Deep feature extraction and its application for hailstorm detection in a large collection of radar images. SIViP 13, 541–549 (2019).

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  • Hailstorm detection
  • Convolutional neural network
  • Deep feature extraction