Autonomous Detection and Classification of Lunar Minerals Using a Convolutional Neural Network Based Framework for the SUCR DALI Project

Noah Carter; Eric Z Tucker; Michael Grant; M Nurul Abedin

NASA NTRS · 20260004595

Autonomous Detection and Classification of Lunar Minerals Using a Convolutional Neural Network Based Framework for the SUCR DALI Project

Noah Carter·Eric Z Tucker·Michael Grant·M Nurul Abedin

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Abstract

NASA’s long-term goal is to deploy humans to the Moon and, from there, advance human exploration to Mars, with Artemis missions as pivotal milestones. Raman spectroscopy can uniquely identify minerals, compounds, water states, and other materials, providing distinctive fingerprints for classification. A Raman instrument has been successfully deployed and utilized on the Mars surface via the Perseverance rover, but has not yet been utilized at the lunar surface The SUCR DALI project is working towards developing a Raman spectroscopy instrument to be applied in various lunar mission concepts, including within the Artemis program. The objective of my research is to assist in the maturation of the proposed SUCR DALI lunar Raman instrument through the development of an autonomous detection and classification model capable of identifying minerals and water states on the Moon’s surface.

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Convolutional Neural Networks mineralogy lunar science fluorescence spectroscopy Machine Learning Raman Spectroscopy

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Noah Carter, Eric Z Tucker, Michael Grant, M Nurul Abedin. Autonomous Detection and Classification of Lunar Minerals Using a Convolutional Neural Network Based Framework for the SUCR DALI Project. https://ntrs.nasa.gov/citations/20260004595

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