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Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net

Runzhe Tao1, *, Yonghong Zhang1, Lihua Wang1, Pengyan Cai1, Haowen Tan2

1 School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 Department of Computer Engineering, Chosun University, Gwangju, 501759, Korea.

* Corresponding Author: Runzhe Tao. Email: email.

Computers, Materials & Continua 2020, 65(3), 2455-2474.


Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of U-Net to residual mapping. Then, in order to solve the problem of the network that would produce semantic differences when directly concentrated with low-level and high-level features, we use dense skip pathways to reuse feature maps of different layers as inputs for concatenate neural networks feature layers from different depths. Finally, according to the characteristics of precipitation clouds, the pooling layer of U-Net was replaced by a convolution operation to realize the detection of small precipitation clouds. It was experimentally concluded that the Pixel Accuracy (PA) and Mean Intersection over Union (MIoU) of the improved U-Net on the test set could reach 0.916 and 0.928, the detection of precipitation clouds over Tibet were well actualized.


Cite This Article

R. Tao, Y. Zhang, L. Wang, P. Cai and H. Tan, "Detection of precipitation cloud over the tibet based on the improved u-net," Computers, Materials & Continua, vol. 65, no.3, pp. 2455–2474, 2020.


cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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