Remote sensing change detection (RSCD) is often disturbed by nuisance appearance variations, which can introduce pseudo-changes and degrade the reliability of predicted change masks. Robust change localization therefore requires that such spurious responses be suppressed while the structural integrity of change regions in complex, high-resolution scenes is maintained. We propose TriFusion-CD, a tri-branch framework that fuses complementary sources of information for reliable change localization. The first branch uses MobileSAM to provide global semantic guidance that promotes spatially coherent predictions. The second branch adopts the CLIP-ResNet50 image encoder with a change-aware enhancement module to extract detail-sensitive change features. The third branch performs frequency decomposition and interacts frequency features with CLIP text embeddings via cross-attention, producing a structural–semantic prior to suppress appearance-induced pseudo-changes. We further design a Semantic Attention Fusion Module (SAFM) to inject MobileSAM semantics into CLIP change features through cross-attention with learnable residual scaling. In addition, an Attention-Modulated Decoder (AMD) translates the fused guidance into multi-scale attention maps and performs progressive top-down refinement, extracting more spatially complete change regions. On the challenging SYSU-CD, JL1-CD, and CDD datasets, which exhibit diverse change patterns and frequent appearance-induced pseudo-changes, TriFusion-CD achieves 72.48% IoU/84.04% F1 on SYSU-CD, 66.04% IoU/79.54% F1 on JL1-CD, and 96.41% IoU/98.17% F1 on CDD, demonstrating strong performance.
Wang et al. (2026) studied this question.