## 4 Experimental Results and Analysis
### Train  
As graph neural networks (GCNs) rely on the topological structure of graphs to propagate information, different superpixel scales directly determine the \"graph resolution\" and the \"complexity of the adjacency graph\", thereby affecting the model's representation capacity and convergence. Therefore, we conducted a comprehensive ablation study. First, we transformed the training set--composed of real satellite target tracking images--into graph structures using five different segmentation scales: 100, 196, 256, 324, and 1024, and saved the corresponding superpixel information. Then, we applied the algorithm described in Section 3.2 to compute the inter-frame adjacency matrices for all the graph-structured data. Finally, we input the graph structure data and inter-frame adjacency matrices into our designed multilayer graph convolutional neural network for deep learning.  
In addition, as the GCN model proposed in this study follows a supervised learning framework, the region-of-interest labels required during the training phase were generated heuristically. Specifically, we first performed binary mask processing on each frame of the image. In the resulting mask images, the foreground target regions appeared as bright areas (white), while the background remained black. Next, the images were segmented into multiple superpixel regions based on brightness thresholds. Superpixels with brightness values higher than the average were identified as target regions and used to construct the training labels. The dimension of each training label was \(N\times 1\).  
To evaluate the accuracy of the automatically generated labels, we randomly selected 100 image-mask pairs from the training set for manual inspection. The review was conducted by two researchers familiar with image preprocessing and superpixel segmentation. The results showed that over 95% of the samples were highly consistent with the expected target regions. Approximately 5% of the samples exhibited minor errors such as boundary deviation or multitarget interference. To maintain automation and reproducibility in the labeling process, these samples were retained for training.  
For the test set, in order to ensure the objectivity and accuracy of model evaluation, manually annotated mask images were used as ground-truth labels. The manual annotation process involved pixel-level delineation of the target regions by trained researchers, producing high-quality \"gold standard\" labels. All performance evaluation metrics reported in this paper(such as IoU, Dice, SSIM, and RMSE) were calculated based on these manually annotated labels in the test set.  
Considering the stochastic nature of the algorithm, we trained multiple I-GCN prediction models using different numbers of epochs: 50, 100, 200, 500, and 1000. The mean squared error (MSE) was adopted as the loss function, with Adam as the optimizer and a learning rate of 0.001. Our model was validated on a validation set consisting of 500 real satellite target tracking images. The visualized output of the validation set is shown in Figure 5, where we used a difference heatmap to compare the predicted images with the ground-truth mask images.