CSANet: Cross-Scale Axial Attention Network for Road Segmentation

Road segmentation from remote sensing images is an important task in many applications. However, due to the high density of roads and the complex background, the roads are often occluded by trees. This makes accurate road segmentation a challenge task. Most existing road segmentation networks rely on convolutions with small kernels; however, these methods often cannot obtain satisfying results because the long-range dependencies are not captured and the intrinsic relationships between feature maps at different scales are not fully exploited. In this paper, a deep neural network based on a cross-scale axial attention mechanism is proposed to address this problem. This model enables low-resolution features to aggregate global contextual information from high-resolution features. Among them, the axial attention mechanism realizes global attention by using vertical and horizontal attention sequentially. With this strategy, the dense long-range dependencies can be captured with extremely low computational cost. The cross-scale mechanism enables the model to effectively combine the high-resolution fine-grained features and the low-resolution coarse-grained features. The proposed method enables the network to propagate the information without losing details. Our method achieves IoUs of 58.98 and 65.28 on the Massachusetts Roads dataset and DeepGlobe dataset and outperforms other methods.