Automatic Extraction of Urban Water Bodies Based on Domestic High Resolution Satellite Images
Water extraction based on satellite images has become an important direction of remote sensing applications,and the traditional automatic extraction of water bodies based on remote sensing has achieved good results in water bodies with simple characteristics,but there are different degrees of misreferences and omissions for water bodies in complex urban environments,especially the impact of building shadows and small water bodies.In order to solve this problem,this study uses domestic high-resolution satellite(Jilin No.1)images,and selects a deep learning model with both classification and pixel positioning capabilities,and makes urban water samples and iterative training,so as to realize the automatic rapid and accurate extraction of water bodies in Jiading urban area,which solves the extraction problem of urban water bodies to a certain extent.
urban water bodydomestic high-resolution imagedeep learningU-Net
NETL
NSTL
万方数据
