Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning

2026-03-25

AI Summary

This study proposes a low-cost, high-accuracy approach to correlate lunar meteorite spectra with lunar surface mineralogy, thereby enhancing the fidelity of remote sensing–based mineral mapping. By integrating micro-hyperspectral imaging (Specim FX10) of the Bishal 010 meteorite with push-broom hyperspectral data acquired via a ground-based Celestron 8SE telescope, the work combines support vector machine classification (RBF kernel), K-means clustering, and spectral angle mapper (SAM) techniques. Crucially, it introduces LIME-based explainable AI to identify diagnostically critical wavelengths, enabling the first cross-scale spectral linkage between meteorite samples and lunar terrain. The framework achieves 93.7% classification accuracy for olivine and pyroxene in the meteorite and 88% clustering accuracy on ground-based lunar observations, with results showing strong consistency with Chandrayaan-1 Moon Mineralogy Mapper (M³) data, establishing a novel paradigm for high-fidelity, all-sky multi-target mineralogical mapping.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Computer Vision: Remote Sensing / Geospatial AI
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Machine learning and data science for the Web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the Bechar010 Lunar meteorite with ground-based lunar HSI and supervised Machine Learning(ML) to generate high-fidelity mineralogical maps. A 3mm thin section of Bechar010 was imaged under a microscope with a 30mm focal length lens at 150mm working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X $\times$ Y $\times$ $λ$ = $791 \times 1024 \times 224$, 0.24mm $\times$ 0.2mm resolution) across 400-1000}nm (224 bands, 2.7nm spectral sampling, 5.5nm full width at half maximum spectral resolution) using a Specim FX10 camera. Ground-based lunar HSI was captured with a Celestron 8SE telescope (3km/pixel), yielded a data cube ($371 \times 1024 \times 224$). Solar calibration was performed using a Spectralon reference ({99}\% reflectance {<2}\% error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved {93.7}\% classification accuracy(5-fold cross-validation) for olivine ({92}\% precision, {90}\% recall) and pyroxene ({88}\% precision, {86}{\%} recall) in Bechar 010. LIME analysis identified key wavelengths (e.g., 485nm, {22.4}\% for M3; 715nm, {20.6}\% for M6) across 10 pre-selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. SAM analysis revealed angles from 0.26 radian to 0.66 radian, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of Lunar data identified 10 mineralogical clusters ({88}\% accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper ($\rm M^3$) data (140m/pixel, 10nm spectral resolution).A novel push-broom HSI approach with a telescope achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping.

Problem

• Meteorite Spectra
• Lunar Surface Composition
• Hyperspectral Imaging
• Mineralogical Mapping
• Machine Learning

Innovation

• Hyperspectral Imaging
• Machine Learning
• Lunar Meteorite
• Mineralogical Mapping
• Push-broom Spectroscopy