SCAFNet: A Semantic Compensated Adaptive Fusion Network for Remote Sensing Images Change Detection

Current CNN-Transformer hybrid methods for remote sensing change detection aim to address the limitations of CNNs’ constrained receptive fields and Transformers’ local detail insensitivity. However, these methods suffer from semantic misalignment and non-adaptive fusion between the dual branches, resulting in persistent sensitivity to pseudo-changes. To address these issues, we propose SCAFNet, a Semantic Compensated Adaptive Fusion Network, featuring three core components: 1) The Semantic Compensation Module (SCM) that aligns local-global features via cross-attention to resolve spatial-semantic mismatches; 2) The CNN-Transformer Feature Adaptive Fusion (CTFAF) module improving feature integration by…

• NN
  National Natural Science Foundation of China

  Awards: No.61702347, 61702347
• HP
  Hubei Provincial Central Guidance Local Science and Technology Development Project

  Awards: F2022210007, CXZX2025049