@Article{cmc.2020.011526,
AUTHOR = {Runzhe Tao, Yonghong Zhang, Lihua Wang, Pengyan Cai, Haowen Tan},
TITLE = {Detection of Precipitation Cloud over the Tibet Based on the  Improved U-Net},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {3},
PAGES = {2455--2474},
URL = {http://www.techscience.com/cmc/v65n3/40181},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.011526}
}