D-Resunet: Resunet and Dilated Convolution for High Resolution Satellite Imagery Road Extraction

Abstract

Reliably extracting information from satellite imagery is a difficult problem with many practical applications. One specific case of this problem is the task of automatically detecting roads. Road extraction from satellite images has been a hot research topic in the past decade. In this paper, we propose a semantic segmentation neural network, named D-ResUnet, which adopts U-Net structure, residual learning, and dilated convolutions for road area extraction. The network is built with ResUnet architecture and has dilated convolution layers in its center part. ResUnet architecture combines the strengths of residual units and feature concatenate, which help to ease training of networks and facilitate information propagation. Dilation convolution is a powerful tool that can enlarge the receptive field of feature points without reducing the resolution of the feature maps. We test our network and compare it with U-Net and ResUnet based road extraction methods. The proposed approach outperforms all the comparing methods, which demonstrates its superiority over recently developed state of the arts.