A Saliency-Aware Deep Network for Narrow Road Extraction of High-Resolution Remote Sensing Imagery

Road extraction from high-resolution remote sensing imagery is important and efficacious due to deep learning. However, most methods face challenges in capturing narrow roads, i.e. rural roads. In this paper, we propose a saliency-aware deep network (SAN) for narrow road extraction. Specifically, a multi-scale context module is employed to extract contextual features in different scales, and a global context module is followed to aggregate the above multi-scale context features, to improve the connectivity of narrow roads. In addition, motivated by visual saliency, a saliency-aware module is proposed to highlight roads during skip connections, to further separate narrow roads from complex backgrounds. In the experiments, SAN is compared with some state-of-the-art methods by using the DeepGlobe road dataset and achieves better performances, especially for narrow roads, which proves its superiority.