Hyperspectral Imaging and Machine Learning Advance Lunar Mineral Mapping

This study presents a novel approach to lunar mineralogical mapping by integrating laboratory hyperspectral imaging (HSI) of a lunar meteorite with ground-based lunar HSI and supervised machine learning. The use of a Support Vector Machine (SVM) model, trained on expert-labeled spectra, demonstrates high classification accuracy for key lunar minerals like olivine and pyroxene. The validation of the K-means clustering results against Chandrayaan-1 Moon Mineralogy Mapper (M3) data further strengthens the reliability of the method. The application of Local Interpretable Model-agnostic Explanations (LIME) analysis to identify key wavelengths associated with specific minerals adds another layer of interpretability to the results. This integrated framework offers a cost-effective alternative to traditional methods for generating high-fidelity mineralogical maps of the lunar surface. The push-broom HSI approach with a telescope achieving 0.8 arcsec resolution for lunar spectroscopy is particularly inspiring for full-sky multi-object spectral mapping. This research has significant implications for future lunar exploration and resource utilization efforts, providing valuable insights into the composition and distribution of lunar resources. The ability to accurately map lunar minerals is crucial for identifying potential sites for in-situ resource utilization (ISRU) and supporting the development of a sustainable lunar economy. The study highlights the potential of combining laboratory analysis, remote sensing data, and machine learning techniques to advance our understanding of the Moon and other planetary bodies.

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