SynerCD: Synergistic Tri-Branch and Vision-Language Coupling for Remote Sensing Change Detection

RSCD faces persistent challenges in high-resolution imagery due to complex spatial structures, temporal heterogeneity, and semantic ambiguity. While deep learning methods have significantly advanced the field, most existing models still rely on static and homogeneous processing, treating all channels and modalities equally, which limits their capacity to capture fine-grained semantic shifts or adapt to region-dependent variations. To address these issues, we propose SynerCD, a unified Siamese encoder–decoder framework that introduces dynamic, content-adaptive perception through channel decoupling, frequency-domain enhancement, and vision-language collaboration. The encoder employs a Tri-branch Synergistic Coupling (TSC) module that dynamically rebalances channel responses and captures multi-scale spatial-frequency dependencies via Mamba-based long-sequence modeling and wavelet decomposition. The decoder integrates a vision-aware language-guided attention (VAL-Att) module, which adaptively modulates visual-textual fusion using CLIP-based semantic prompts to guide attention toward meaningful change regions. Extensive experiments on four benchmark datasets verify that SynerCD achieves superior localization accuracy and semantic robustness, establishing a dynamic and adaptive paradigm for multimodal change detection.