U-Shaped Network with Hybrid Convolution and Block Calculation for Road Extraction

Extracting road information from remote sensing imagery is essential for numerous applications. Traditional methods have heavily depended on manual feature extraction, but recent progress has shifted the focus towards deep learning-based approaches. However, many existing methods primarily focus on the continuity of local roads, neglecting challenges associated with road shape extraction. These challenges include interference from shadows, buildings, vehicles, and complex backgrounds, often resulting in discontinuous results. To tackle these issues, this paper introduces a novel U-shaped deep learning network that integrates a Hybrid Convolution Module and Block Loop Attention to achieve robust road extraction, which called HLU-Net. The Hybrid Convolution Module, with its two parallel branches featuring convolution kernels of varying scales, not only broadens the receptive field to capture comprehensive contextual information but also concentrates on local details for accurate feature extraction. Furthermore, each branch employs unique convolution techniques to reduce computational redundancy and enhance operational efficiency. In addition, the proposed Block Loop Attention mechanism effectively models the relationships between elements within feature vectors, capturing intrinsic dependencies, and thus facilitating more efficient computations without merely reducing complexity. Experiments conducted on the DeepGlobe and CITY-OSM datasets demonstrate that our approach generally surpasses several state-of-the-art road extraction methods.