FD-ProtoSCD: Semantic Change Detection in High-Resolution Remote Sensing Images via Frequency-Domain Disentanglement and Dynamic Prototype Learning

Abstract

Semantic Change Detection (SCD) in high-resolution remote sensing images is challenged by appearance-induced pseudo-changes and highly imbalanced class transitions. To address these coupled difficulties, we propose FD-ProtoSCD, a decoupled SCD framework that combines Frequency-Domain Change Disentanglement (FDCD) with a Dynamic Class Prototype Decoder (DCPD). FDCD decomposes bi-temporal features with a learnable Fourier-domain mask and emphasizes low-frequency structural differences while constraining high-frequency appearance variations in unchanged regions. DCPD maintains online semantic prototype banks and uses focal prototype contrastive learning to strengthen rare transition recognition. Under the reported comparison protocols, experiments on SECOND, Hi-UCD, and LEVIR-CD show improved semantic consistency and binary change localization over the included representative baselines, with an SCD score of 45.60 on SECOND and F1 scores of 46.23 and 94.24 on the Hi-UCD transfer and LEVIR-CD settings, respectively.