Burden-Free Distillation From Foundation Model for Efficient Remote Sensing Change Detection

Applying vision foundation models to remote sensing change detection (CD) has attracted extensive research attention. These studies employ inherent general knowledge from vision foundation models to enhance CD performance. Existing methods explicitly employ the foundation model as a feature extractor while designing additional learnable modules to bridge the task gap. However, these methods substantially increase the computational burden and memory demand in the inference. This paper therefore focuses on addressing the core challenge of effectively leveraging the knowledge from vision foundation models to enhance CD performance while maintaining computational efficiency. Instead of explicitly utilizing the foundation model, we propose Burden-Free Distillation (BFD), an architecture-agnostic foundation model-based distillation framework for efficient CD. BFD transfers the general knowledge from foundation models to task-specific models, thereby eliminating the dependency on foundation models during inference. Specifically, BFD transfers the foundation model knowledge through Dual-temporal Feature Matching module (DFM). This module enables multi-level feature alignment by computing pixel-wise spatial similarity between the foundation models’ general features and the CD models’ task-specific features. Additionally, we leverage patch contrastive distillation, which transfers localized structural patterns to the CD model to further mitigate task discrepancies between foundation models and CD models. We conduct extensive experiments across multiple foundation models and CD architectures, experimental results demonstrate that BFD effectively adapts the knowledge of foundation models to CD tasks without additional computational burden. Compared to other foundation model-based CD methods, BFD reduces the model parameters by 80.3% and improves IoU by 1.78% on the S2Looking dataset. The code is available at https://github.com/Younger-hua/Burden-Free-Distillation.