Spatial Group-wise Enhanced U-Net for Road Extraction from High-resolution Remote Sensing Images
Road extraction is an integral part of modern road network planning.Recently,many deep learning methods have been applied in this field.However,it is still a problem to extract the road area accurately while maintaining continuity due to the oc-clusion of vehicles and the shadows of trees and buildings.This paper presents a novel road extraction network,the Spatial Group-wise Enhanced U-Net(SGEU-Net),builts on two parts,which are an improved Encoder-Decoder U-Net and the Spatial Group-wise Enhanced(SGE)module.The SGE module can significantly improve the spatial distribution of different semantic sub-features within the groups and produce a more considerable statistical variance,enhancing feature learning in semantic regions.The improved algorithm is experimented on the Massachusetts road dataset,and the results show that the proposed algorithm im-proves the extraction of roads from remote sensing images compared with current state-of-the-art algorithms.
deep learningroad extractionhigh-resolution imageryspatial group-wise enhanced attention
万方数据
维普
