Dual-Task Network for Road Extraction From High-Resolution Remote Sensing Images

In high-resolution remote sensing images, road scale diversity and occlusions caused by shadows, buildings, and vegetation often pose challenges for road extraction. Currently, end-to-end models constructed using deep convolutional neural networks are widely used in road extraction and have significantly improved the accuracy of this task. However, the connectivity and completeness of their results require improvement. This article proposes a dual task-driven deep convolutional neural network constructed by combining road shape patterns and scale differences. The mainline task is road-surface segmentation, the encoder of which employs residual convolution for feature extraction. The decoder comprises a multiscale and multidirection strip convolution module, the output of which is the final extraction result. The splitting task is road centerline extraction, the input features of which come from the coding layer of the road-surface segmentation branches. The intermediate features are incorporated into the decoder of the road-surface segmentation branches, to fully exploit the road centerline and thus improve the road-surface segmentation result connectivity. Implementation of the proposed method on the CHN6-CUG and DeepGlobe datasets reveals superior performance to comparative methods as regards quantitative evaluation metrics; evident advantages for road coverings, road intersections, and low-scale roads; greater model portability; and better small-sample learning capability.

Automated summary

This article proposes a dual task-driven deep convolutional neural network for road extraction, which combines road shape patterns and scale differences. The network utilizes residual convolution and multiscale convolution to improve feature extraction and connectivity, resulting in superior performance compared to existing methods.