## 5 Conclusions and Future Work  
This paper applied the GCN algorithm to the task of orbital satellite region tracking and prediction. For the first time, a combination of intra-frame and inter-frame graph structures was used, allowing the graph convolutional network to perform spatiotemporal prediction tasks. This approach solved the issue of reduced computational efficiency in target tracking models based on CNN networks due to data redundancy.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experimental Methods**} & \multicolumn{3}{c}{**Evaluating Metrics**} & \\ \cline{2-7}  & **IoU** & **Dice** & **SSIM** & **CC** & **RMSE** & **R\({}^{2}\)** \\ \hline ConvLSTM & 0.719 & 0.8137 & 0.8550 & 0.8319 & 17.34 & 0.6545 \\
3D-CNN & 0.7178 & 0.8126 & 0.8429 & 0.8331 & 16.68 & 0.6744 \\ Optical Flow & 0.7139 & 0.81 & 0.8537 & 0.8341 & 8.41 & 0.7919 \\ pix2 pix GAN & 0.7113 & 0.8090 & 0.905 & 0.8192 & 17.82 & 0.6068 \\ U-net & 0.718 & 0.8132 & 0.9064 & 0.8317 & 17.16 & 0.6543 \\ F-GCN & 0.728 & 0.8224 & 0.9126 & 0.9189 & 7.92 & 0.8649 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of different experimental methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Superpixel Scale**} & \multicolumn{3}{c}{**Epoch**} \\ \cline{2-5}  & **50** & **100** & **200** & **500** & **1000** \\ \hline
100 & 0.86 & 0.96 & 0.91 & 0.87 & 0.87 \\
196 & 1.22 & 1.26 & 1.22 & 1.19 & 1.18 \\
256 & 1.48 & 1.63 & 1.48 & 1.64 & 1.63 \\
324 & 2.17 & 2.66 & 2.52 & 2.21 & 2.65 \\
1024 & 14.78 & 14.79 & 15.43 & 14.73 & 15.57 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time cost under different segmentation scales and epochs (ms).  
The proposed algorithm was tested at the Changchun Observatory of the National Astronomical Observatory, Chinese Academy of Sciences, and the impact of different superpixel segmentation scales on the performance of the proposed model was verified. The results showed that the model significantly improved both the tracking accuracy and bandwidth. Our algorithm not only increased the data processing speed but also significantly improved the bandwidth and tracking accuracy of the tracking system.  
The orbital satellite region tracking model proposed in this paper provides an efficient and robust solution for satellite target tracking, but there is still room for further research and optimization. For example, during the process of constructing graph structure data, we only used the SLIC method. In the future, more efficient and accurate superpixel segmentation methods could be explored to further reduce computational costs. Similarly, in terms of model construction, increasing the depth of graph convolution layers could be considered to capture more complex spatiotemporal features, thereby improving tracking accuracy.  
In the future, we will also incorporate tracking data for satellites with different orbits and types, expand the training dataset, and improve the model's generalization capability.  
Conceptualization, S.W. and Y.M.; methodology, S.W.; software, R.W.; validation, Y.J.; formal analysis, X.D.; investigation, M.L.; resources, Y.M.; data curation, G.W.; writing--original draft preparation, S.W.; writing--review and editing, X.D.; visualization, M.I. and J.L.; supervision, Y.M.; project administration, X.D.; funding acquisition, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation Project of Jilin Province (20240101300J)C.  
Not applicable.  
The dataset used in this study is subject to a confidentiality agreement and cannot be shared. However, the core model architecture and training configuration files are available at: [[https://github.com/ws874694232/I2-GCN-architecture](https://github.com/ws874694232/I2-GCN-architecture)]([https://github.com/ws874694232/I2-GCN-architecture](https://github.com/ws874694232/I2-GCN-architecture)) (accessed on 2 June 2025).  
The authors declare no conflicts of interest.