SAM-FE: Segment Anything Model Guided Feature Enhancement for Semantic Change Detection of Remote Sensing Images

Semantic change detection (SCD) is a crucial research topic in remote sensing. To achieve high-precision semantic segmentation results, Segment Anything Model-Guided Feature Enhancement (SAM-FE) is proposed. SAM-FE utilizes Mobile-SAM to extract features from bi-temporal remote sensing images (RSIs). In addition, the cross-temporal feature aggregation module (CTFA), the multiscale contextual information fusion module (MCIF), and the change feature enhancement module (CFE) are utilized to enhance the general features and the representation of the change information of the RSIs, thus improving the accuracy of change detection. Experimental results indicate that SAM-FE significantly outperforms the existing methods in both the Second datasets and MusSCD, with F1 of 0.6241 and 0.8316, respectively. Meanwhile, SAM-FE maintains lower parameters, demonstrating its superiority and practicality.