# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 4 Experiment and Results
### Comparative Experiments
#### 4.2.3 Comparative Experiments on GZ-CD  
The FC-Siam-Diff model showed the lowest performance on the dataset, with an IoU of just 51.34% and an F1 score of 67.85%. The FC-EF and FC-Siam-Conc models demonstrated comparable results on this dataset. In contrast, SAM-CD achieved an IoU of 78.42% and an F1 score of 87.41%. Our Siamese-SAM model outperformed SAM-CD, achieving an IoU of 79.83% and an F1 score of 88.79%, marking improvements of 1.41% and 1.38%, respectively. Additionally, Siamese-SAM also demonstrated significant improvements in precision (+2.81%), recall (+0.01%), Kappa (+1.04%), and overall accuracy (+0.26%). These results highlight the effectiveness of our proposed model in detecting changes with greater precision and reliability on the GZ-CD dataset. The detailed quantification of model evaluation metrics is presented in Table 6.  
Figure 12 shows the comparative visualization results on the GZ-CD dataset, with key regions highlighted using red boxes. In the first set of data, which involves the newly constructed factory on open land, all models perform relatively well, but our Siamese-SAM model provides the smoothest edge detection for the factory, capturing the fine details more effectively than the others. In the second set, where vegetation in the lower-right corner was transformed into a building, only our model was able to detect the majority of the changed area, while other models detected only a small portion of the change. In the third set, which features the disappearance of a factory in the lower-left corner, our model not only detected the change but also minimized the false positives, resulting in \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **PR (\%)** & **RC (\%)** & **OA (\%)** & **Kappa (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline FC-EF [49] & 81.77 & 66.76 & 88.65 & 66.38 & 58.11 & 73.50 \\ FC-Siam-Diff [49] & 83.34 & 55.67 & 86.92 & 59.01 & 50.09 & 66.75 \\ FC-Siam-Conc [49] & **85.18** & 66.61 & 89.36 & 68.17 & 59.69 & 74.76 \\ ChangeNet [50] & 77.09 & 71.18 & 88.21 & 66.41 & 58.75 & 74.02 \\ BIT [32] & 81.50 & 73.90 & 89.89 & 71.02 & 63.29 & 77.52 \\ SAGNet [51] & 79.90 & 81.03 & 90.36 & 74.75 & 66.98 & 80.46 \\ AMFNet [52] & 80.12 & 82.23 & 90.95 & 76.07 & 69.55 & 81.17 \\ SAM-CD [38] & 81.33 & 82.50 & 91.13 & 76.65 & 69.76 & 81.91 \\ Ours & 82.12 & **83.11** & **91.65** & **77.95** & **70.17** & **82.61** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experiments on the SYSU-CD dataset (best results are highlighted in bold type). fewer errors compared to the other models. These results further demonstrate the superior performance of Siamese-SAM in capturing subtle details, handling complex boundaries, and reducing errors in change detection tasks. Figure 11: Three sets of visualization results of different algorithms on the SYSU-CD dataset. T1_img and T2_img are images at different times, label is the ground truth. a–e are BIT, SAGNet, AMFNet, SAM-CD, and Siamese-SAM, respectively. Figure 12: Three sets of visualization results of different algorithms on the GZ-CD dataset. T1_img and T2_img are images at different times, label is the ground truth. a–e are BIT, SAGNet, AMFNet, SAM-CD, and Siamese-SAM, respectively. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Method** & **PR (\%)** & **RC (\%)** & **OA (\%)** & **Kappa (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline FC-EF [49] & 82.01 & 64.02 & 95.74 & 69.64 & 56.14 & 71.91 \\ FC-Siam-Diff [49] & 80.25 & 58.77 & 95.25 & 65.35 & 51.34 & 67.85 \\ FC-Siam-Conc [49] & 84.85 & 63.34 & 95.91 & 70.38 & 56.91 & 72.54 \\ ChangeNet [50] & 80.26 & 81.52 & 96.43 & 78.92 & 67.91 & 80.89 \\ BIT [32] & 89.90 & 77.16 & 97.08 & 81.46 & 71.00 & 83.04 \\ SAGNet [51] & 84.95 & 85.01 & 97.22 & 83.45 & 73.88 & 84.98 \\ AMFNet [52] & 87.63 & 83.95 & 97.42 & 84.33 & 75.06 & 85.75 \\ SAM-CD [38] & 87.90 & 86.93 & 97.87 & 86.72 & 78.42 & 87.41 \\ Ours & **90.71** & **86.94** & **98.13** & **87.76** & **79.83** & **88.79** \\ \hline \hline \end{tabular}
\end{table}