Surface River Extraction from Remote Sensing Images based on Improved U-Net

Abstract

The accurate extraction of surface rivers is of great significance to ecology, residence and so on. In view of the incomplete recognition of river edge contour in the surface river extraction of remote sensing image in the classical deep learning network U-Net, the ability of the network to learn and retain the detailed information of feature map is enhanced by strengthening the attention mechanism and introducing the densely connected Atrous Spatial Pyramid Pooling on the basis of U-Net. The experimental results show that the Pixel Accuracy of water extraction results by this method is 92.1%, and the Mean Intersection Over Union is up to 90.3%, the improved algorithm can effectively extract accurate surface river information.