A Unified Brightness Temperature Features Analysis Framework for Mapping Mare Basalt Units Using Chang’e-2 Lunar Microwave Sounder (CELMS) Data

Abstract

The brightness temperature (TB) features extracted from Chang’e-2 Lunar Microwave Sounder (CELMS) data represent the passive microwave thermal emission (MTE) from the lunar regolith at different depths. However, there have been few studies assessing the importance and contribution of each TB feature for mapping mare basalt units. In this study, a unified framework of TB features analysis is proposed through a case study of Mare Fecunditatis, which is a large basalt basin on the eastern nearside of the Moon. Firstly, TB maps are generated from original CELMS data. Next, all TB features are evaluated systematically using a range of analytical approaches. The Pearson coefficient is used to compute the correlation of features and basalt classes. Two distance metrics, normalized distance and J-S divergence, are selected to measure the discrimination of basalt units by each TB feature. Their contributions to basalt classification are quantitatively evaluated by the ReliefF method and out-of-bag (OOB) importance index. Then, principal component analysis (PCA) is applied to reduce the dimension of TB features and analyze the feature space. Finally, a new geological map of Mare Fecunditatis is generated using CELMS data based on a random forest (RF) classifier. The results will be of great significance in utilizing CELMS data more widely as an additional tool to study the geological structure of the lunar basalt basin.

Keywords

• Mare Fecunditatis
• brightness temperature
• feature assessment
• mare basalt
• machine learning
• dimension reduction