Extracting animal migration pattern from weather radar observation based on deep convolutional neural networks

The weather radar can operate in all weathers and all time, and has a large coverage area. Besides monitoring the weather, the weather radar can receive other echoes including biological echoes. In order to utilise weather radar biological monitoring capability, recognising and classifying local ins...

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Bibliographic Details
Main Authors: Cheng Hu, Siwei Li, Rui Wang, Kai Cui, Dongli Wu, Shuqing Ma
Format: Article
Language:English
Published: Wiley 2019-09-01
Series:The Journal of Engineering
Subjects:
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0041
Description
Summary:The weather radar can operate in all weathers and all time, and has a large coverage area. Besides monitoring the weather, the weather radar can receive other echoes including biological echoes. In order to utilise weather radar biological monitoring capability, recognising and classifying local insect and bird echoes is one of the biggest obstacles for analysing their migration, foraging, and reproduction activities. Here, a pixel-wise classification method based on the fully convolutional network (FCN) is proposed which is trained by the radar reflectivity and the spectral width images. Moreover, to increase the biometric detection accuracy, the region growing method is combined for achieving the region edge alignment. Finally, the proposed method is validated based on the real weather radar datasets in Yantai. The FCN training results have a high pixel accuracy of 92.96%, and the region growing method performs well in the edge alignment.
ISSN:2051-3305