Insights into Lunar Mineralogy: An Unsupervised Approach for Clustering of the Moon Mineral Mapper (M3) spectral data

TL;DR

This paper introduces an unsupervised machine learning approach using autoencoders and clustering to map and analyze lunar mineral distribution from hyperspectral data, enhancing lunar mineral exploration.

Contribution

It presents a novel combination of convolutional variational autoencoders and k-means clustering for spectral feature extraction and mineral mapping of the Moon's surface.

Findings

Clusters correspond to major lunar minerals like plagioclase and olivine.

The method's clusters align with existing mineral maps from Kaguya.

Unsupervised learning effectively reveals lunar mineralogy.

Abstract

This paper presents a novel method for mapping spectral features of the Moon using machine learning-based clustering of hyperspectral data from the Moon Mineral Mapper (M3) imaging spectrometer. The method uses a convolutional variational autoencoder to reduce the dimensionality of the spectral data and extract features of the spectra. Then, a k-means algorithm is applied to cluster the latent variables into five distinct groups, corresponding to dominant spectral features, which are related to the mineral composition of the Moon's surface. The resulting global spectral cluster map shows the distribution of the five clusters on the Moon, which consist of a mixture of, among others, plagioclase, pyroxene, olivine, and Fe-bearing minerals across the Moon's surface. The clusters are compared to the mineral maps from the Kaguya mission, which showed that the locations of the clusters overlap with the locations of high wt% of minerals such as plagioclase, clinopyroxene, and olivine. The paper demonstrates the usefulness of unbiased unsupervised learning for lunar mineral exploration and provides a comprehensive analysis of lunar mineralogy.