  The application of infrared hyperspectral imagery to geological problems is
becoming more popular as data become more accessible and cost-effective.
Clustering and classifying spectrally similar materials is often a first step
in applications ranging from economic mineral exploration on Earth to planetary
exploration on Mars. Semi-manual classification guided by expertly developed
spectral parameters can be time consuming and biased, while supervised methods
require abundant labeled data and can be difficult to generalize. Here we
develop a fully unsupervised workflow for feature extraction and clustering
informed by both expert spectral geologist input and quantitative metrics. Our
pipeline uses a lightweight autoencoder followed by Gaussian mixture modeling
to map the spectral diversity within any image. We validate the performance of
our pipeline at submillimeter-scale with expert-labelled data from the Oman
ophiolite drill core and evaluate performance at meters-scale with partially
classified orbital data of Jezero Crater on Mars (the landing site for the
Perseverance rover). We additionally examine the effects of various
preprocessing techniques used in traditional analysis of hyperspectral imagery.
This pipeline provides a fast and accurate clustering map of similar geological
materials and consistently identifies and separates major mineral classes in
both laboratory imagery and remote sensing imagery. We refer to our pipeline as
"Generalized Pipeline for Spectroscopic Unsupervised clustering of Minerals
(GyPSUM)."
