Superpixel-Tokenized and Frequency-Modulated Hybrid CNN–Transformer for Remote Sensing Semantic Segmentation

Remote sensing semantic segmentation is fundamental for fine-grained urban scene understanding, which in turn provides pixel-level semantic insights for urban development and environmental surveillance. However, existing hybrid segmentation architectures fail to incorporate intrinsic geometric and physical priors, inevitably leading to structural fragmentation, boundary ambiguity, and spatial misalignment of heterogeneous features. Therefore, we propose a Superpixel-Tokenized and Frequency-Modulated Hybrid CNN–Transformer network (SFCT-Net) for remote sensing semantic segmentation. The proposed network integrates superpixel tokens and high-frequency constraints to preserve structural integrity and boundary precision. First, our Superpixel-Tokenized Linear Position Attention (STLPA) module replaces rigid window tokens with semantic superpixels to ensure object integrity with linear computational complexity. Second, we construct a Frequency-Modulated Deformable Edge Refinement (FMDER) module that leverages high-frequency spectral priors to modulate deformable sampling, achieving robust boundary recovery. Finally, we develop the Spatial–Semantic Feature Coupling (SSFC) module, which employs a dual-branch strategy to correct spatial drift and align deep semantic features with shallow details. Experiments conducted on our self-built Taiyuan Satellite Remote Sensing Dataset (TSRSD) along with the ISPRS Vaihingen and Potsdam benchmark datasets demonstrate that our proposed SFCT-Net delivers state-of-the-art performance and efficiency by fusing superpixel and frequency priors for robust structural and boundary recovery.