# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 4 Experiment and Results
### Comparative Experiments  
#### 4.2.1 Comparative Experiments on LEVIR-CD  
For the comparison experiment on the LEVIR-CD dataset, we performed a comprehensive evaluation of our proposed Siamese-SAM model by comparing it with multiple other models. All methods were trained using the same approach to ensure fairness and the reliability of the results. The detailed quantification of model evaluation metrics is presented in Table 4.  
Empirical evaluation demonstrates that FC-EF exhibits the poorest performance among deep-learning-based change detection architectures, achieving substantially lower F1 score (80.98%) and IoU (68.03%) compared to baseline methods. Compared to SAM-CD, our Siamese-SAM achieves a 1.22% improvement in F1 score and a 2.1% improvement in IoU. Additionally, there are significant improvements in precision (+0.86%), recall (+1.57%), Kappa (+1.28%), and overall accuracy (+0.1%). Although Siamese-SAM slightly outperforms SAM-CD on the LEVIR-CD dataset, its improvements on SYSU-CD and GZ-CD are more significant, demonstrating the strong generalization ability of the model on different datasets. At the same time, we conducted a large amount of visual analysis to prove the superiority of the model.  
For the visual analysis of the results, we selected three datasets and highlighted the key regions with red boxes to demonstrate the advantages of our model. The four categories are indicated by different colors: white represents true positives (areas where a change occurred and the model correctly identified the change), black represents true negatives (areas where no change occurred and the model correctly identified no change), red represents false positives (areas where no change occurred but the model incorrectly identified a change), and green represents false negatives (areas where a change occurred but the model failed to identify it).  
Figure 10 illustrates the visualization results for three different sets of data on the LEVIR-CD dataset. In the first dataset, our Siamese-SAM model was the only one able to detect the newly built house on the empty lot with a high degree of completeness, accurately capturing the entire change area. In the second dataset, our model demonstrated superior performance in edge detection, providing the most precise delineation of the changed house's boundary, outperforming other models in handling such fine details. In the third dataset, our model made the fewest mistakes in identifying the changed areas, showing its ability to minimize false positives and false negatives. The experimental results unequivocally demonstrate the methodological excellence of Siamese-SAM, particularly in achieving superior detection capabilities for sub-pixel-scale changes and fine-grained spatial contexts where conventional deep learning architectures exhibit inherent limitations.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **PR (\%)** & **RC (\%)** & **OA (\%)** & **Kappa (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline FC-EF [49] & 84.78 & 77.50 & 98.14 & 80.00 & 68.03 & 80.98 \\ FC-Siam-Diff [49] & 88.54 & 79.87 & 98.45 & 83.17 & 72.39 & 83.98 \\ FC-Siam-Conc [49] & 87.52 & 83.66 & 98.56 & 84.79 & 74.74 & 85.55 \\ ChangeNet [50] & 91.36 & 86.16 & 99.11 & 88.22 & 79.67 & 88.69 \\ BIT [32] & 91.81 & 87.62 & 99.18 & 89.24 & 81.27 & 89.67 \\ SAGNet [51] & 92.24 & 88.10 & 99.22 & 89.72 & 82.02 & 90.12 \\ AMFNet [52] & 92.56 & 88.87 & 99.26 & 90.29 & 82.95 & 90.68 \\ SAM-CD [38] & 93.29 & 89.67 & 99.32 & 91.09 & 84.24 & 91.45 \\ Ours & **94.15** & **91.24** & **99.42** & **92.37** & **86.34** & **92.67** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative experiments on the LEVIR-CD dataset (best results are highlighted in bold type).