FWENet:a deep convolutional neural network for flood water body extraction based on SAR images  

作　　者：Jingming Wang Shixin Wang Futao Wang Yi Zhou Zhenqing Wang Jianwan Ji Yibing Xiong Qing Zhao 

机构地区：[1]Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,People’s Republic of China [2]University of Chinese Academy of Sciences,Beijing,People’s Republic of China [3]Hainan Research Institute,Aerospace Information Research Institute,Chinese Academy of Sciences,Sanya,People’s Republic of China [4]School of Geography Science and Geomatics Engineering,Suzhou University of Science and Technology,Suzhou,People’s Republic of China

出　　处：《International Journal of Digital Earth》2022年第1期345-361,共17页国际数字地球学报（英文）

基　　金：supported by Finance Science and Technology Project of Hainan Province[number ZDYF2021SHFZ103]and Strategic Priority Research Program of the Chinese Academy of Sciences[number XDA19090123].

摘　　要：As one of the most severe natural disasters in the world,floods caused substantial economic losses and casualties every year.Timely and accurate acquisition of flood inundation extent could provide technical support for relevant departments in the field of flood emergency response and disaster relief.Given the accuracy of existing research works extracting flood inundation extent based on Synthetic Aperture Radar(SAR)images and deep learning methods is relatively low,this study utilized Sentinel-1 SAR images as the data source and proposed a novel model named flood water body extraction convolutional neural network(FWENet)for flood information extraction.Then three classical semantic segmentation models(UNet,Deeplab v3 and UNet++)and two traditional water body extraction methods(Otsu global thresholding method and Object-Oriented method)were compared with the FWENet model.Furthermore,this paper analyzed the water body area change situations of Poyang Lake.The main results of this paper were as follows:Compared with other five water body extraction methods,the FWENet model achieved the highest water body extraction accuracy,its F1 score and mean intersection over union(mIoU)were 0.9871 and 0.9808,respectively.This study could guarantee the subsequent research on flood extraction based on SAR images.

关 键 词：Deep learning flood water body extraction SAR Poyang Lake 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]