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

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Abstract

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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Acknowledgements

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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Authors and Affiliations

  1. University of Alabama in Huntsville, Huntsville, USA

    Iksha Gurung & Chao Peng

  2. National Aeronautics and Space Administration (NASA), Marshall Space Flight Center, Huntsville, USA

    Manil Maskey & Rahul Ramachandran

Authors
  1. Iksha Gurung
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  2. Chao Peng
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  3. Manil Maskey
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  4. Rahul Ramachandran
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Corresponding author

Correspondence to Chao Peng.

Additional information

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). https://doi.org/10.1007/s11760-018-1380-z

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  • DOI: https://doi.org/10.1007/s11760-018-1380-z

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