## 1 Introduction
It offered advantages such as a lightweight model, ease of training, and deployment, making it suitable for resource-constrained environments. However, due to the lack of explicit temporal sequence modeling, this method had certain limitations in capturing target motion trends. Optical Flow explicitly modeled displacement information of targets through the optical flow field, offering strong physical interpretability and being well suited for rigid targets or static background scenarios [18]. However, the performance of this method was highly dependent on the quality of optical flow estimation, and its prediction accuracy degraded significantly in the presence of occlusion, complex deformation, or low-contrast regions. Pix2 Pix GAN, as a type of conditional generative adversarial network, performed well in preserving image structure and edge details, generating high-quality images [19]. It was particularly suitable for scenarios requiring high visual fidelity. However, the GAN training process itself was relatively unstable, and the generated results were sensitive to training strategies and loss function weight settings. Compared with the aforementioned traditional spatiotemporal modeling methods, graph convolutional networks (GCNs) demonstrated unique advantages in modeling target motion trends. Unlike convolution-based models such as ConvLSTM and 3D-CNN, which relied on fixed grid structures, GCNs naturally adapted to non-Euclidean data structures, making them particularly suitable for dynamic graph structures constructed from centroid trajectories extracted from sequential mask images. This graph-based representation explicitly modeled inter-frame structural dependencies, effectively enhancing the model's ability to capture temporal continuity of the target. Furthermore, spatiotemporal graph convolutional networks (ST-GCNs), by decoupling temporal and spatial modeling, exhibited stronger performance in modeling long-term dependencies compared to U-Net recurrent structures. In contrast to optical flow methods--which could only handle apparent motion between two frames and lacked predictive capability--GCNs enabled future trend prediction based on multiframe graph structures, expanding both the depth and foresight of modeling. Compared with generative adversarial networks such as Pix2 Pix GAN, GCNs did not rely on pixel-level aligned data, and could effectively model using sparse yet robust graph structures (e.g., centroids or edge features), thereby reducing computational overhead and enhancing generalization. More importantly, GCNs offered strong model interpretability: their explicit adjacency relationships intuitively reflected inter-frame motion linkages, while also supporting multimodal feature fusion at the node level (e.g., velocity, category, optical flow), granting high extensibility and flexibility. As a result, GCN-based methods for target prediction and tracking had attracted increasing attention [20, 21, 22]. Although existing GCN methods had been widely applied in areas such as action recognition, multiobject tracking, and traffic prediction, direct application to LEO satellite imagery targets acquired from ground stations posed adaptation challenges. Satellite images typically had low frame rates, small targets, and blurry edges, while GCNs required robust graph structures and were prone to underfitting. Due to the high speed of satellite target movement and large inter-frame displacements, GCNs--being sensitive to \"node locality\"--often suffered from collapsed graph connectivity and unstable training. To address these challenges, we proposed a novel Intra-Inter Graph Convolutional Network (I\({}^{2}\)-GCN) for tracking LEO satellite targets. This framework simultaneously learned intra-frame and inter-frame graph structures to systematically capture the spatiotemporal evolution of the target. By integrating both intra-frame and inter-frame graph features, the proposed model effectively improved tracking robustness and communication efficiency.