A Graph-Based Dual Convolutional Network for Automatic Road Extraction from High Resolution Remote Sensing Images

Abstract

Recently, deep-learning-based methods, especially deep convolutional neural networks (DCNNs), have effectively shown state-of-the-art performance in road extraction from high resolution remote sensing images (HRSI). However, due to the loss of location information and global context information, most existing DCNNs are inadequate for extracting tiny roads or roads which are severely occluded, leading to incomplete and discontinuous results. To address this problem, this paper proposes a graph-based dual convolutional network (GDCNet), which combines graph convolutional network (GCN) and convolutional neural network (CNN). In this model, GCN and CNN branches perform feature learning on large-scale irregular regions and small-scale regular regions, and generate complementary spatial-spectral features at superpixel and pixel levels, respectively. Then, a graph decoder is utilized to propagate features between graph nodes and image pixels, enabling the GCN and CNN to collaborate in a single network. Extensive experiments on two benchmark datasets demonstrate that the proposed GDCNet is competitive compared with other state-of-the-art methods both qualitatively and quantitatively, and is effective against the incomplete and discontinuous problems of the extracted roads.