FDMamba: Frequency-Driven Dual-Branch Mamba Network for Road Extraction From Remote Sensing Images

Road extraction from remote sensing imagery is crucial for a variety of applications, including transportation monitoring, disaster response, and urban planning. However, existing methods often fail to accurately delineate sparse, curvilinear, and boundary-blurred road structures in high-resolution images, leading to incomplete detail preservation and inadequate contextual understanding. To address these challenges, we propose a novel frequency-driven dual-branch mamba network (FDMamba) for precise road extraction from remote sensing imagery. The proposed FDMamba integrates frequency-aware modeling with a dual-branch architecture, enabling collaborative learning of fine-grained edge details and global spatial dependencies. Specifically, FDMamba comprises three key modules: a Fourier reconstruction attention mechanism (FRAM) to enhance high-frequency boundary information and low-frequency structural representation; a rotation-aware mamba module (RaMamba) that leverages multipath state space modeling for robust directional perception of road structures; and a phase-guided feature fusion module (PFFM) for effective cross-scale alignment and fusion of high- and low-frequency features. Furthermore, to mitigate the issue of blurred or ambiguous boundaries, we introduce a hybrid loss function that combines binary cross-entropy (BCE) loss, focal loss, and frequency-aware loss, explicitly guiding the model to focus on edge structure and multifrequency complementary information. Extensive experiments on three benchmark datasets, CHN6-CUG, DeepGlobe, and Massachusetts, demonstrate that FDMamba consistently outperforms state-of-the-art methods in terms of $F1$ -score and intersection over union (IoU), achieving superior boundary clarity and structural continuity while preserving overall geometric integrity. The code is available at https://github.com/darkseid-arch/RE-FDMamba