Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net |
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Authors: | Runzhe Tao Yonghong Zhang Lihua Wang Pengyan Cai Haowen Tan |
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Affiliation: | 1.School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 Department of Computer Engineering, Chosun University, Gwangju, 501759, Korea. |
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Abstract: | 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. |
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Keywords: | U-net fy-4a precipitation cloud dense skip connections residual network |
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