## 4 Experimental Results and Analysis
### Model Evaluation and Predictive Assessment  
Structure similarity (SSIM) [27] and correlation coefficient (CC) [28] were chosen to evaluate the proposed model. SSIM measures the overall similarity of two images by comparing their correlation in brightness, contrast, and structure. \[SSIM(t,p)=\frac{(2\mu_{t}\mu_{p}+c_{1})(2\sigma_{tp}+c_{2})}{(\mu_{t}^{2}+\mu_ {p}^{2}+c_{1})(\sigma_{t}^{2}+\sigma_{p}^{2}+c_{2})} \tag{20}\]  
where \(t\) was the true image data of the tracking target, \(p\) was the image data predicted by the model, and \(\mu_{t}\), \(\mu_{p}\), \(\sigma_{t}^{2}\), \(\sigma_{p}^{2}\), and \(\sigma_{tp}\) were the statistics of \(t\) and \(p\), which represented mean, variance, and covariance. \(c_{1}\) and \(c_{2}\) were area constants which were 0.01 and 0.03, respectively. CC measured the correlation between two sets of data by calculating the cosine of the angle between them in their sample space. It was defined as follows:  
\[CC(S_{\text{t}},S_{\text{p}})=\frac{\text{cov}(S_{\text{t}},S_{\text{p}})}{ \sigma(S_{\text{t}})\sigma(S_{\text{p}})} \tag{21}\]  
where \(\sigma(S_{\text{t}})\) and \(\sigma(S_{\text{p}})\) were the standard deviations of the graph features predicted by the model and the real target graph features, respectively. The use of a joint evaluation method based on SSIM and CC could comprehensively assess the model's performance in terms of structural preservation and pixel-level consistency, helping to identify different types of distortions and improving the accuracy of the evaluation. Then, to assess the accuracy and fit of the predicted model results from different perspectives, root mean squared error (RMSE) [29] and R-square (R\({}^{2}\)) [30] were used for joint evaluation. RMSE is an indicator that measures the average difference between the predicted values and the actual values. It was obtained by calculating the average deviation of corresponding pixels between the original image data and the visualized graph feature image data processed in Section 3.4. The calculation formula was  
\[\text{RMSE}=\sqrt{\frac{1}{m\times n}\sum_{i=0}^{m-1}\sum_{j=0}^{n-1}\left[I_{ l}(i,j)-I_{p}(i,j)\right]^{2}} \tag{22}\]  
where \(m\) and \(n\) were the height and width of the image, and \(I_{t}(i,j)\) and \(I_{p}(i,j)\) represented the pixel values at position \((i,j)\) in the original and predicted images, respectively. R\({}^{2}\) was used to assess the strength of the relationship between the predicted values and the actual  
Figure 5: Visual comparison between predicted target masks and ground truth annotations for four representative test samples. For each case (**a–d**), the left column shows the predicted binary mask, the middle column shows the corresponding ground truth, and the right column shows the pixel-wise difference (Gray Diff) along with the calculated Intersection over Union (IoU) and Dice coefficient. These examples demonstrate the model’s prediction accuracy under different target sizes and locations. values. It was obtained by calculating the ratio of the residual sum of squares to the total sum of squares between the images. The calculation formula was  
\[R^{2}=1-\frac{\sum_{i=1}^{M}\left(I_{t}(i,j)-I_{p}(i,j)\right)^{2}}{\sum_{i=1}^{M }\left(I_{t}(i,j)-\overline{I_{t}}\right)^{2}} \tag{23}\]  
where \(\overline{I_{t}}\) was the average value of all the pixels in image \(I_{t}\). In satellite image target prediction and tracking, RMSE could quantify the model's prediction error, while R\({}^{2}\) reflected the model's ability to explain the changes in the target's position. By combining both, a more comprehensive evaluation of the model's performance could be achieved. Finally, to quantitatively evaluate the region overlap accuracy between the predicted images and the ground-truth masks, we adopted two classical metrics: intersection over union (IoU) and the Dice coefficient. IoU measures the ratio between the intersection and the union of the predicted and ground-truth regions.