Road Extraction from GF-1 Remote Sensing Images Based on Dilated Convolution Residual Network with Multi-Scale Feature Fusion

This paper aimed to solve the problems of road edge detail information loss and inaccurate road extraction due to multiple downsampling operations of the fully convolutional neural network. Thus, a road extraction method of GF-1 remote sensing images based on dilated convolution residual network with multiscale feature fusion is proposed. First, numerous labels for road extraction are generated through visual interpretation. Second, dilated convolution and multiscale feature perception modules are introduced in each residual block of the residual network, namely, ResNet-101, to enlarge the receptive field of the feature points without reducing the feature map resolution and losing the detailed edge information. Third, through superposition fusion and upsampling operations, the road feature maps of various sizes are fused to obtain the feature maps of the original resolution size. Finally, for classification, the feature maps are input into the Sigmoid classifier. The experimental results indicate that the proposed method is more accurate than the conventional fully convolutional neural network models, with the accuracy rate being more than 98%. The proposed method effectively preserves the integrity and detailed edge information of the road area.