Aiming at the problem that feature information is easily lost and lacks attention to target features when the LinkNet network model performs road image segmentation tasks, a high resolution remote sensing image road extraction method based on an improved residual network in LinkNet is proposed. Replace the residual block (Res Block) in the coding area of the original LinkNet model with a dense block (Dense Block). The dense connection method reduces the loss of feature information during the transmission process, and builds convolutional attention after each dense block. Units are used to improve the model’s learning ability of target features. Finally, the atrous space pyramid pooling module is used to connect the encoding area and the decoding area to expand the receptive field while also accepting multi-scale target feature information. Experiments show that the accuracy, average intersection ratio and F1-score of this method on the DeepGlobe data set are 82.16%, 83.21% and 81.65%, respectively, which are all better than similar networks. By comparing the extracted road network results, the algorithm has significantly improved the completeness and accuracy of road network extraction under tree shelters and building shadows.