LR-RoadNet: A long-range context-aware neural network for road extraction via high-resolution remote sensing images

Road extraction from high-resolution remote sensing images (HRSIs) has great importance in various practical applications. However, most existing road extraction methods have considerable limitation in capturing long-range shape feature of road, and thus, they are ineffective in extracting road region under complex scenes. To address this issue, a novel model called long-range context-aware road extraction neural network (LR-RoadNet) is proposed. LR-RoadNet takes advantage of strip pooling to capture long-range context from horizontal and vertical directions, aiming to improve continuity and completeness of road extraction results. Specifically, the LR-RoadNet consists of two parts: strip residual module (SRM) and strip pyramid pooling module (SPPM). The SRM is built based on residual unit, in which the strip pooling is employed to learn general and long-range road feature from input image. Then, the SPPM is used to obtain long-range feature from multiple scales by multiple parallel strip pooling operations. More importantly, a structural similarity (SS) loss function is introduced to further explore road structure for optimizing LR-RoadNet. The experimental results show that the proposed method achieves great improvement than other state-of-the-art methods on three challenging datasets, Cheng-Roads, Zimbabwe-Roads and Mass-Roads.