Robot Learning for Real-World Applications

1D Profiles vs. Spectral Images: A Comparative Study of Machine Learning Models for Mineral and Rock Classification

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Abstract

The rapid identification of minerals is critical for real-time geological analysis. This study investigates the efficacy of machine learning models in classifying mineral and rock samples using high-speed Raman sensors. We evaluate three distinct data representation strategies: (1) 1D spectral profiles, (2) 2D Raman spectral images, and (3) a fused multi-modal approach combining both spatial and spectral features. Using a diverse dataset of geological samples, we benchmark several model architectures to determine the trade-offs between computational efficiency and classification accuracy. Our results demonstrate how spatial context from imaging can enhance identification compared to traditional 1D methods, while also identifying the scenarios where signal-only processing remains optimal. This work provides a framework for selecting the most effective data representation for high-speed, automated mineralogical mapping.

How to Cite:

Gottam, S., Findl, M., Galler, R., Sedlazeck, K. & Rueckert, E., (2026) “1D Profiles vs. Spectral Images: A Comparative Study of Machine Learning Models for Mineral and Rock Classification”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 176-179.