Road extraction from remote sensing images by combining attention and context fusion

Abstract

Aiming at the problem that the objects in remote sensing images are complex, and the roads are long, thin, continuously distributed and easily blocked, a road extraction model for remote sensing images combining attention and context fusion (ACFD-LinkNet) is proposed. The model is based on the D-LinkNet network. First, a strip attention module is used after the last convolutional layer of the D-LinkNet network encoder to enhance the feature extraction ability of roads of different scales, better capture the global features of the road, and capture the long-distance information of the road; secondly, a context fusion module (CFM) is proposed and added to the feature transfer part of the network codec to predict the road connection between adjacent pixels, fuse the road information between different levels of the context, and solve the problem of obstacles blocking the road connection; finally, the cross entropy loss function and Dice loss function of the improved model are set with multi-loss function hyperparameter weight distribution to solve the imbalance of positive and negative samples in the data set, and the best segmentation accuracy is obtained by adjusting the weight ratio. Experiments were conducted on DeepGlobe and CHN6-CUG datasets, and the comprehensive index F1 value reached 86.76%、92.12%, which was improved compared with the D-LinkNet model . 3.96%、1.13%In addition, compared with Unet, Deeplabv3+, A 2-FPN and other networks, it has the best performance.