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

TL;DR

This paper introduces GyPSUM, an unsupervised workflow combining autoencoders and Gaussian mixture models for clustering hyperspectral images of geological targets, validated on Earth and Mars data, improving speed and accuracy.

Contribution

The paper presents GyPSUM, a novel fully unsupervised clustering pipeline that integrates expert input and quantitative metrics for hyperspectral geological image analysis.

Findings

Accurately clusters geological materials in laboratory and orbital data

Identifies major mineral classes consistently across datasets

Provides a fast, generalizable clustering method for hyperspectral imagery

Abstract

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)."