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.

Semantic segmentation is a challenging task in computer vision, which requires both context information and rich spatial detail. To this end, most methods introduce low-level features for spatial detail. However, low-level features lack global information. Too much low-level features will disturb the segmentation result. In this paper, we extract low-level features guided by abstract semantic features to improve segmentation results. Specifically, we propose a Pixel-wise Attention Module (PAM) to select low-level features adaptively and a Dual Channel-wise Attention Fusion Module (DCAFM) to fuse the context information further. These two modules use the attention mechanism from a more macro perspective, which is not limited to the inter-layer feature adjustments. There are not complicated and redundant processing modules in our architecture. By using features efficiently, the complexity of the network was significantly reduced. We evaluate our approach on Cityscapes, PASCAL VOC 2012, and PASCAL Context datasets, and we achieve 82.3% Mean IoU on PASCAL VOC 2012 test dataset without pre-training on the MS-COCO dataset.