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

arXiv: https://arxiv.org/abs/2106.13315 · HTML: https://mlsanity.com/paper/2106.13315/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 48 references that are in the map.
- First posted on arXiv 24 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Dive into Deep Learning](https://mlsanity.com/paper/2106.11342/) (2021)
- [On the Impact of the Activation Function on Deep Neural Networks Training](https://mlsanity.com/paper/1902.06853/) (2019)
- [Unsupervised Segmentation of Hyperspectral Images Using 3D Convolutional Autoencoders](https://mlsanity.com/paper/1907.08870/) (2019)
- [Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion](https://mlsanity.com/paper/1704.07961/) (2017)
- [Machine learning based hyperspectral image analysis: A survey](https://mlsanity.com/paper/1802.08701/) (2018)
- [Hyperspectral Subspace Identification Using SURE](https://mlsanity.com/paper/1606.00219/) (2016)
- [Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero and NE Syrtis](https://mlsanity.com/paper/1909.02387/) (2019)

## Built on by (most cited first, 2 of 2)

- [SpecDM: Hyperspectral Dataset Synthesis with Pixel-level Semantic Annotations](https://mlsanity.com/paper/2502.17056/) (2025)
- [Remote sensing framework for geological mapping via stacked autoencoders and clustering](https://mlsanity.com/paper/2404.02180/) (2024)

## Abstract (arXiv)

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)."