Semantic-Guided Fuzzy Difference Refinement For Remote Sensing Semantic Change Detection

Semantic change detection of bi-temporal remote sensing images plays an important role in urban development monitoring, environmental assessment, and land management. Recent deep learning methods have substantially improved change localization and semantic recognition. However, existing methods still struggle to preserve weak temporal evidence and make reliable decisions when real semantic changes exhibit low visual contrast. To address these challenges, we propose the Semantic-Guided Fuzzy Difference Refinement Network, termed SFDRNet, which is organized around two coordinated mechanisms: Temporal Semantic Calibration (TSC) and Uncertainty-Aware Semantic Difference Refinement (USDR). TSC constructs reliable temporal evidence by jointly modeling spatial structure and frequency statistics, and then calibrates the semantic representations of the two dates. USDR injects the calibrated semantic difference into the change representation and applies learnable fuzzy reasoning with complementary local and global evidence to refine ambiguous responses before change classification. In this way, SFDRNet first strengthens weak change evidence and then resolves the uncertainty that remains before prediction. Extensive experiments on two publicly available datasets demonstrate the effectiveness of SFDRNet. Further ablation, contrast-stratified, and uncertainty analyses validate the effectiveness of the two mechanisms in recognizing low contrast changes and refining ambiguous predictions.