Machine Learning Fusion Multi-Source Data Features for Classification Prediction of Lunar Surface Geological Units

Abstract

Taking the Chang’e-4 and Chang’e-5 landing areas as the study areas, this study extracts the geological unit information from the regional USGS geological map, as well as the feature information such as topography and geomorphology, material composition and mineral abundance from Chang’e-2 DOM and DEM, wide angle camera (WAC) and Kaguya multi-band imager data. By applying methods including the statistical-based estimation of mutual information of data and the integrated-algorithmic-model-based evaluation of feature importance to this extracted information, we screen the significant features and construct a high-precision classification model by combining machine learning algorithm with important features of sample data. The practical application of the multi-classification prediction on the complex geological units in the two study areas achieves 97.9% and 95.1% accuracy. At the same time, the significant characteristics of the study area are mined, and the rules and knowledge associated with the geological evolution of the study area are obtained. In this study, we carry out research on quantitative prediction and identification of lunar surface geological units based on large samples and construct a high-precision multi-classification model to achieve automatic classification and prediction on large sample geological units with high accuracy. This method provides a new idea for the predicted mapping of geological units of lunar global digital mapping. In addition, it helps to fully exploit the useful information in the data and enrich the knowledge regarding the formation and evolution of the Moon.