AD-RoadNet: An Auxiliary-Decoding Road Extraction Network Improving Connectivity While Preserving Multiscale Road Details

Obtaining Road information from high-resolution remote sensing images is gaining attention in intelligent transportation systems. Existing road extraction methods tend to improve road connectivity with graph convolution or global attention, however, ignore the damage of introduced excessive effective receptive field (ERF) to multiscale road details. In this study, we propose an auxiliary-decoding road extraction network named AD-RoadNet, which decouples multiscale road representation and connectivity improvement based on two modules; the hybrid receptive field module (HRFM) and the topological feature representation module (TFRM). The HRFM is introduced in the encoder to emphasize target road features through adaptively matching the receptive field (RF) size for various scale roads, thus, beneficial for multiscale road representation. The TFRM is introduced in an auxiliary decoder to represent topological features with the position information encoded in the shared encoder and then helps the main decoder reason occluded roads, thus improving connectivity. Between the encoder and main decoder. The proposed model has a similar parameter scale as HRNetV2 and outperforms the state-of-the-art ResUnet, D-LinkNet, and HRNetV2 by 3.34%, 2.03%, and 1.53% in the mean intersection of union on DeepGlobe road dataset. Ablation analysis, inference size matter, and the robustness for unseen occlusion scenarios, low-quality labels, and various quality inference images are further presented to evaluate the proposed AD-RoadNet.