FDENet: Frequency-Guided Dual-Encoder Network for Building Footprint Extraction From Remote Sensing Images

Building footprint extraction is a fundamental task in remote sensing image analysis, with wide-ranging applications in smart city development, urban planning, and disaster response. Despite recent advances, existing methods still struggle to simultaneously capture fine-grained boundary details, corresponding to high-frequency information such as sharp edges and textures, and maintain the integrity of overall building shapes, which depends on low-frequency global structural information. These limitations often lead to blurred or inaccurate boundaries and incomplete building representations, especially in complex urban environments with densely packed or irregularly shaped buildings. This article presents a novel Frequency-Guided Dual-Encoder Network (FDENet) for building footprint extraction, which combines a graph-aware wavelet decomposition with a frequency-controlled visual state-space model within a dual-encoder attention framework. Specifically, the proposed framework incorporates a Graph-Constrained Wavelet Transformer Block to establish a dual-encoder structure that operates across both spatial and frequency domains. The high-frequency branch utilizes residual convolution blocks to enhance texture and boundary feature representation, while the low-frequency branch integrates a Frequency-Selective Visual State-Space Model to capture global structural information. In addition, a Mixed-Frequency Attention Mechanism is introduced to facilitate the deep fusion of high- and low-frequency features, improving their interaction and complementarity. To address the persistent issue of blurred boundaries, a cross-stage adaptive fusion loss function is designed, incorporating an Edge-Aware Loss to further enhance boundary precision. Comprehensive experiments on multiple public building extraction datasets demonstrate that FDENet achieves state-of-the-art performance, significantly improving both boundary sharpness and structural consistency.