SDFCNet: A Spatial-Domain and Frequency-Domain Collaborative Network for Building Extraction in High-Resolution Remote Sensing Images

To address the low accuracy in building boundary extraction from high-resolution remote sensing images, this paper proposes a spatial-frequency collaborative building extraction network named SDFCNet, which integrates boundary information from both the frequency domain and the spatial domain. The High-Resolution Feature Processing Module (HRFPM) is designed to fuse multi-scale features within the encoder, which compensates for detail loss due to downsampling and provides richer edge information for subsequent extraction. Additionally, a High-Frequency Signal Processing Module (HFSMP) extracts edge features from high-resolution characteristics and fuses them with high-frequency signals from the original image, which enhances the precision and completeness of boundary extraction by constraining the decoder’s feature boundaries. Finally, a deep supervision strategy is introduced to provide auxiliary supervision for both high-frequency signals and decoder outputs. The experimental results on the INRIA and WHU datasets demonstrate that this approach outperforms mainstream building extraction networks in multiple evaluation metrics, offering enhanced accuracy and completeness in building boundary extraction.