  The binary segmentation of roads in very high resolution (VHR) remote sensing
images (RSIs) has always been a challenging task due to factors such as
occlusions (caused by shadows, trees, buildings, etc.) and the intra-class
variances of road surfaces. The wide use of convolutional neural networks
(CNNs) has greatly improved the segmentation accuracy and made the task
end-to-end trainable. However, there are still margins to improve in terms of
the completeness and connectivity of the results. In this paper, we consider
the specific context of road extraction and present a direction-aware residual
network (DiResNet) that includes three main contributions: 1) An asymmetric
residual segmentation network with deconvolutional layers and a structural
supervision to enhance the learning of road topology (DiResSeg); 2) A
pixel-level supervision of local directions to enhance the embedding of linear
features; 3) A refinement network to optimize the segmentation results
(DiResRef). Ablation studies on two benchmark datasets (the Massachusetts
dataset and the DeepGlobe dataset) have confirmed the effectiveness of the
presented designs. Comparative experiments with other approaches show that the
proposed method has advantages in both overall accuracy and F1-score. The code
is available at: https://github.com/ggsDing/DiResNet.
