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The application of infrared hyperspectral imagery to geological problems isbecoming more popular as data become more accessible and cost-effective.Clustering and classifying spectrally similar materials is often a first stepin applications ranging from economic mineral exploration on Earth to planetaryexploration on Mars. Semi-manual classification guided by expertly developedspectral parameters can be time consuming and biased, while supervised methodsrequire abundant labeled data and can be difficult to generalize. Here wedevelop a fully unsupervised workflow for feature extraction and clusteringinformed by both expert spectral geologist input and quantitative metrics. Ourpipeline uses a lightweight autoencoder followed by Gaussian mixture modelingto map the spectral diversity within any image. We validate the performance ofour pipeline at submillimeter-scale with expert-labelled data from the Omanophiolite drill core and evaluate performance at meters-scale with partiallyclassified orbital data of Jezero Crater on Mars (the landing site for thePerseverance rover). We additionally examine the effects of variouspreprocessing techniques used in traditional analysis of hyperspectral imagery.This pipeline provides a fast and accurate clustering map of similar geologicalmaterials and consistently identifies and separates major mineral classes inboth laboratory imagery and remote sensing imagery. We refer to our pipeline as"Generalized Pipeline for Spectroscopic Unsupervised clustering of Minerals(GyPSUM)."

Generalized Unsupervised Clustering of Hyperspectral Images of Geological Targets in the Near Infrared