Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net
作者机构：School of AutomationNanjing University of Information Science and TechnologyNanjing210044China Department of Computer EngineeringChosun UniversityGwangju501759Korea
出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)
年 卷 期：2020年第65卷第12期
页 面：2455-2474页
核心收录：
学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：U-net fy-4a precipitation cloud dense skip connections residual network
摘 要：Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring.U-Net,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 ***,the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of U-Net to residual ***,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 ***,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 *** 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.
维普期刊数据库