Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network

Hongyun YANG; Fengyan WANG

doi:10.11999/JEIT190098

Volume 41 Issue 10

Oct. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(10): 2373-2381

Hongyun YANG, Fengyan WANG. Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2373-2381. doi: 10.11999/JEIT190098

Citation:

Hongyun YANG, Fengyan WANG. Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2373-2381. doi: 10.11999/JEIT190098

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Meteorological Radar Noise Image Semantic Segmentation Method Based on Deep Convolutional Neural Network

doi: 10.11999/JEIT190098

Hongyun YANG^,,
Fengyan WANG

College of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (U1833107), The National Science and Technology Major Project (2012ZX03002002)

Received Date: 2019-02-17
Rev Recd Date: 2019-06-04

Available Online: 2019-06-10

Publish Date: 2019-10-01

Abstract

Abstract

Considering the problem that the scattering echo image of the new generation Doppler meteorological radar is reduced by the noise echoes such as non-rainfall, the accuracy of the refined short-term weather forecast is reduced. A method for semantic segmentation of meteorological radar noise image based on Deep Convolutional Neural Network(DCNN) is proposed. Firstly, a Deep Convolutional Neural Network Model (DCNNM) is designed. The training set data of the MJDATA data set are used for training, and the feature is extracted by the forward propagation process, and the high-dimensional global semantic information of the image is merged with the local feature details. Then, the network parameters are updated by using the training error value back propagation iteration to optimize the convergence effect of the model. Finally, the meteorological radar image data are segmented by the model. The experimental results show that the proposed method has better denoising effect on meteorological radar images, and compared with the optical flow method and the Fully Convolutional Networks (FCN), the method has high recognition accuracy for meteorological radar image real echo and noise echo, and the image pixel precision is high.
- Meteorological radar,
- Deep learning,
- Image semantic segmentation,
- Image denoising,
- Convolutional Neural Network(CNN)

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References(19)

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