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

Abstract

Abstract 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.

Keywords

• deep convolutional neural networks
• long‐range context
• pyramid pooling module
• road extraction
• strip pooling