Change detection in remote sensing images is a critical task that requires effectively capturing both global and differential information between bitemporal or more images. Recent progress in foundational vision models, like the Segment Anything Model (SAM), has led to significant improvements in feature extraction. However, these models do not have specific mechanisms designed to effectively utilize global and differential information for change detection tasks. To address this limitation, we propose Siamese-SAM, a novel Siamese network incorporating SAM as the encoder for each input image. To enhance feature representations, we introduce three specialized modules: the Global Information Enhancement Module (GIEM) to refine global representations, the Differential Information Enhancement Module (DIEM) to emphasize differential features, and the Differential Global Information Fusion Module (DGIF) to integrate global and differential information effectively. Our model is evaluated on three benchmark datasets: LEVIR-CD, SYSU-CD, and GZ-CD, achieving state-of-the-art performance. Specifically, Siamese-SAM attains F1 scores of 92.67%, 82.61%, and 88.79% and IoU scores of 86.34%, 70.17%, and 79.83%, respectively, outperforming conventional approaches.