SAM-FDN: A SAM Fine-Tuning Adaptation Remote Sensing Change Detection Method Based on Fourier Frequency Domain Analysis Difference Reinforcement

Change detection is a pivotal task in remote sensing information extraction, and leveraging the representation capabilities of large models has emerged as a promising direction in recent research. However, existing large-model-based change detection methods primarily focus on adaptation and fine-tuning strategies, while often overlooking the effective separation of true change information from background content. As a result, these methods still suffer from frequent false alarms and missed detections, especially in complex scenarios. To address these limitations, we propose a SAM fine-tuning adaptation change detection method based on Fourier frequency domain analysis difference reinforcement (SAM-FDN). In this method, we utilize the feature extraction capability of the SAM and adopt a low-rank fine-tuning strategy to construct the feature extraction backbone network of the model, extracting remote sensing image features at different time periods to enhance the model’s cognitive ability towards remote sensing images at different time periods. Furthermore, a Fourier Change Feature Extraction-Separation Module (FCEM) is designed based on Fourier frequency-domain analysis. This module separates high-frequency variation information from low-frequency invariant information, thereby enhancing differential features while suppressing invariant ones, which in turn contributes to more reliable and accurate remote sensing change detection (RSCD). Experiments conducted on three benchmark datasets demonstrate that SAM-FDN consistently outperforms existing state-of-the-art methods across various complex change detection scenarios. Ablation studies further confirm the effectiveness of the proposed coupling strategy between the SAM foundation model and the frequency-domain perception mechanism. In particular, the FCEM significantly improves the separation of meaningful change features and the suppression of irrelevant information, ultimately enhancing the model’s sensitivity to real changes and its overall detection performance.