# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 4 Experiment and Results
### Ablation Experiments on LEVIR-CD  
In the experiments, we tested four SAM series models as encoders, including SAM-b, SAM-l, FastSAM-s, and FastSAM-x. Among them, FastSAM-x achieved the best performance, as shown in the bolded results in Table 1. The results demonstrated that using FastSAM as the encoder significantly outperforms the traditional SAM models. This is primarily because FastSAM leverages an optimized architecture and faster inference capabilities, which enhance its ability to process and adapt to complex remote sensing data, providing superior accuracy compared to the original SAM models. Ablation experiments were conducted on each module using FastSAM-x as the encoder. We added or removed each proposed module to evaluate its contribution to the overall model performance. The experimental results are presented in Table 2, where the impact of each module on the model's accuracy is shown. 1. Ablation experiments of GIEM and DIEM: First, we analyze the contributions of the GIEM and the DIEM to the model's performance. Focusing on the IoU and F1 metrics, we observe the following improvements: the baseline model achieves an IoU of 82.35% and an F1 score of 90.32%. By adding GIEM, the IoU improves to 82.92%, and the F1 score increases to 90.66%. Incorporating DIEM further enhances the performance, with the IoU rising to 83.74% and the F1 score reaching 91.15%. When both GIEM and DIEM are added together, the model achieves an IoU of 84.87% and an F1 score of 91.65%, demonstrating a clear synergy between these two modules. These results suggest that both GIEM and DIEM contribute positively, with DIEM showing a more substantial improvement in both IoU and F1, highlighting the importance of enhancing differential information in change detection tasks. 2. Ablation experiments of DGIF: Building on the results of GIEM and DIEM, we further analyze the impact of the DGIF module. The addition of DGIF yields a significant performance boost, bringing the IoU to 86.34% and the F1 score to 92.67%. Compared to the baseline, the IoU improves by 4.99%, and the F1 score increases by 2.35%. This demonstrates that the DGIF module, which enhances the fusion of global and differential information, plays a crucial role in further improving the model's accuracy. The results from these ablation experiments clearly indicate that enhancing differential information is particularly beneficial for change detection tasks, where capturing subtle variations is critical. In terms of loss parameter selection, we also conducted corresponding ablation experiments, and the results are shown in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**SAM-Encoder** & **PR (\%)** & **RC (\%)** & **OA (\%)** & **Kappa (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline SAM-b & 49.40 & 78.80 & 90.56 & 55.69 & 43.61 & 60.73 \\ SAM-l & 71.88 & 76.30 & 95.04 & 71.29 & 58.77 & 74.03 \\ FastSAM-s & 90.65 & 89.58 & 99.00 & 89.59 & 82.00 & 90.11 \\ FastSAM-x & **94.15** & **91.24** & **99.42** & **92.37** & **86.34** & **92.67** \\ \hline \end{tabular}
\end{table} 
Table 1: Ablation experiments of SAM-Encoder on the LEVIR-CD dataset (the best results are highlighted in bold). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Method** & **PR (\%)** & **RC (\%)** & **OA (\%)** & **Kappa (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline Baseline & 92.01 & 88.68 & 99.03 & 89.81 & 82.35 & 90.32 \\ Baseline + GIEM & 91.97 & 89.39 & 99.06 & 90.17 & 82.92 & 90.66 \\ Baseline + DIEM & 92.82 & 89.54 & 99.11 & 90.68 & 83.74 & 91.15 \\ Baseline + GIEM + DIEM & 93.12 & 90.22 & 99.36 & 91.28 & 84.87 & 91.65 \\ Baseline + GIEM + DIEM + DGIF & **94.15** & **91.24** & **99.42** & **92.37** & **86.34** & **92.67** \\ \hline \end{tabular}
\end{table} 
Table 2: Ablation experiments on the LEVIR-CD dataset (best results are highlighted in bold type).