## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.1 Ablation Study  
We obtained multiple trained models using different superpixel scales and epochs. To validate the effectiveness of the proposed tracking compensation method, we conducted a series of ablation experiments. First, we used the SSIM and CC metrics from Section 4.2 to evaluate all the models. In Figure 6a, the results of comparing the structural similarity between the predicted images generated by models with different superpixel scales and epoch settings and the test set images are presented. From these comparisons, it could be observed that as the superpixel scale increased, the structural similarity between the predicted and test images generally showed an upward trend. However, this trend was neither linear nor absolute. For instance, under a superpixel scale of 324, the outputs from models trained with different epochs did not significantly outperform those obtained at a superpixel scale of 256 in terms of structural similarity. This indicated that, under certain conditions, increasing the superpixel scale did not always lead to improvements in structural similarity, which might also be affected by model training stability and other factors. As shown in the linear correlation coefficient comparison results in Figure 6b, the correlation coefficient between the predicted images and the test set images generally increased with higher superpixel scales. However, for models with superpixel scales of 256, 324, and 1024, the growth trend of the correlation coefficient was relatively flat. Additionally, for both SSIM and CC metrics, performance under the same superpixel scale tended to stabilize as the number of training epochs increased. To further evaluate the impact of superpixel scale on the performance of the E-GCN model, we recorded the MSE loss convergence curves during training under different superpixel scales (100, 196, 256, 324, and 1024), as illustrated in Figure 7. Among these, the model with a superpixel scale of 324 achieved the lowest final MSE loss and exhibited the smoothest convergence curve, indicating excellent prediction stability. In contrast, the model with a superpixel scale of 1024 maintained a relatively high loss throughout the training process, which may have resulted from over-segmentation leading to sparse graph structures, or the limited capacity of the graph neural network to model complex spatial relationships. Figure 6: Quantitative evaluation of prediction results using SSIM (**a**) and CC (**b**) metrics. Figure 7: The MSE loss of models trained with different superpixel scales (100, 196, 256, 324, and 1024). These findings indicated that a moderate number of superpixels (e.g., 256-324) could achieve an effective balance between spatial resolution and modeling stability, thereby enhancing the performance of F-GCN in target prediction and tracking tasks. Finally, this study evaluated the model's prediction error using root mean squared error (RMSE) and R-squared (R\({}^{2}\)) metrics under different superpixel scales. As shown in the Figure 8, with increasing superpixel scale, RMSE exhibited a downward trend, while R\({}^{2}\) continued to rise. When the number of superpixels was 100 and 196, the model struggled to capture local dynamic details due to the coarse segmentation, resulting in high RMSE and low R\({}^{2}\). As the number increased to 256 and 324, the model performance improved significantly, with RMSE decreasing to a lower level and R\({}^{2}\) rising above 0.8. Notably, at a superpixel scale of 1024, R\({}^{2}\) reached its highest value, indicating that the model almost completely captured the target's changing trend. However, RMSE showed a slight increase, suggesting that over-segmentation may introduce noise or detail interference. To validate the effectiveness of the F-GCN model in satellite target prediction and tracking tasks, we conducted a comparative study with existing state-of-the-art methods.