[2603.24323] Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning

Astrophysics > Earth and Planetary Astrophysics arXiv:2603.24323 (astro-ph) [Submitted on 25 Mar 2026] Title:Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning Authors:Fatemeh Fazel Hesar, Mojtaba Raouf, Amirmohammad Chegeni, Peyman Soltani, Bernard Foing, Elias Chatzitheodoridis, Michiel J. A. de Dood, Fons J. Verbeek View a PDF of the paper titled Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning, by Fatemeh Fazel Hesar and Mojtaba Raouf and Amirmohammad Chegeni and Peyman Soltani and Bernard Foing and Elias Chatzitheodoridis and Michiel J. A. de Dood and Fons J. Verbeek View PDF 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 $\times$ $\lambda$ = $791 \times 1024 \times 224$, 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 \...