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

Abstract We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the BECHAR 010 lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A {3}{} thin section of BECHAR 010 was imaged under a microscope with a {30}{} focal length lens at {150}{} working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X × Y × λ = 791 × 1024 × 224, {0.24}{} × {0.2}{} resolution) across {400}{1000}{} (224 bands, {2.7}{} spectral sampling, {5.5}{} FWHM spectral resolution) using a Specim FX10 camera.Ground-based lunar HSI was captured with a Celestron 8SE telescope ({3}{}/pixel), yielded a data cube (371 × 1024 × 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. Local Interpretable Model-agnostic Explanations (LIME) identified key wavelengths (e.g., {485}{}, {22.4}{} for M3; {715}{}, {20.6}{} for M6) across 10 pre selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. Spectral Angle Mapper (SAM) analysis revealed angles from {0.26}{} to {0.66}{}, 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 (M³) data ({140}{}/pixel, {10}{} spectral resolution). A novel push-broom HSI approach with telescope, achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping.