Abstract

Estimating the shape of space objects helps infer key characteristics such as object type, mass, and potential operational status, thereby providing critical decision-making support for space threat assessment. This paper proposes a Physics-Informed Transformer deep learning model for space object shape classification based on photometric time series data. The model achieves a classification accuracy of 89.22% on a hybrid dataset combining simulated and measured data, outperforming eight classical models with an average accuracy improvement of 3.61%. Ablation experiments demonstrate that the introduction of physical gating yields an average accuracy improvement of 1.43%. Using evaluation metrics including the confusion matrix, PR curve, ROC curve, and confidence distribution histogram, we demonstrate that the model possesses high accuracy, strong robustness, and good interpretability.

IPC Classification

Keywords