An Explainable CatBoost Model for Crater Classification Based on Digital Elevation Model

Abstract

The study of secondary craters on the Moon is vital for understanding lunar impact dynamics and surface evolution. However, this task is complicated by sample imbalance, with primary crater samples outnumbering those of secondary craters, and by the reliance on time-intensive manual methods or limited automated techniques. While many previous studies have focused on the manual or automated differentiation of secondary craters, few have addressed the interpretation of variables and models. In this study, we propose a machine-learning-based approach using the CatBoost algorithm to classify craters based on variables extracted from Digital Elevation Model (DEM) data. These variables include those from previous research as well as new ones introduced here, such as slope and density with Gaussian summation. Despite data imbalance and noise, the model achieves a classification accuracy of 0.8788, with a precision of 0.7922, a recall rate of 0.7412, and an F1 score of 0.7658 for secondary craters. To enhance interpretations, Shapley additive explanations (SHAP) and partial dependence plots (PDPs) are applied to evaluate variable importance and visualize the marginal effects of key variables, indicating the density variables playing a key role in crater classification.