Generalized Unsupervised Clustering of Hyperspectral Images of
Geological Targets in the Near Infrared
- URL: http://arxiv.org/abs/2106.13315v1
- Date: Thu, 24 Jun 2021 21:05:10 GMT
- Title: Generalized Unsupervised Clustering of Hyperspectral Images of
Geological Targets in the Near Infrared
- Authors: Angela F. Gao, Brandon Rasmussen, Peter Kulits, Eva L. Scheller,
Rebecca Greenberger, Bethany L. Ehlmann
- Abstract summary: 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.
Here we develop a fully unsupervised workflow for feature extraction and clustering informed by both expert spectral geologist input and quantitative metrics.
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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)."
Related papers
- Hyperspectral Dataset and Deep Learning methods for Waste from Electric and Electronic Equipment Identification (WEEE) [0.0]
We evaluate the performance of diverse deep learning architectures for hyperspectral image segmentation.
Results show that incorporating spatial information alongside spectral data leads to improved segmentation results.
We contribute to the field by cleaning and publicly releasing the Tecnalia WEEE Hyperspectral dataset.
arXiv Detail & Related papers (2024-07-05T13:45:11Z) - SatSynth: Augmenting Image-Mask Pairs through Diffusion Models for Aerial Semantic Segmentation [69.42764583465508]
We explore the potential of generative image diffusion to address the scarcity of annotated data in earth observation tasks.
To the best of our knowledge, we are the first to generate both images and corresponding masks for satellite segmentation.
arXiv Detail & Related papers (2024-03-25T10:30:22Z) - Hyperspectral Image Reconstruction via Combinatorial Embedding of
Cross-Channel Spatio-Spectral Clues [6.580484964018551]
Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands.
We propose to investigate the inter-dependencies in their respective hyperspectral space.
These embedded features can be fully exploited by querying the inter-channel correlations.
arXiv Detail & Related papers (2023-12-18T11:37:19Z) - SaaFormer: Spectral-spatial Axial Aggregation Transformer for
Hyperspectral Image Classification [2.4723464787484812]
Hyperspectral images (HSI) captured from earth observing satellites and aircraft is becoming increasingly important for applications in agriculture, environmental monitoring, mining, etc.
Due to the limited available hyperspectral datasets, the pixel-wise random sampling is the most commonly used training-test dataset partition approach.
We propose a block-wise sampling method to minimize the potential for data leakage.
arXiv Detail & Related papers (2023-06-29T07:55:43Z) - Object Detection in Hyperspectral Image via Unified Spectral-Spatial
Feature Aggregation [55.9217962930169]
We present S2ADet, an object detector that harnesses the rich spectral and spatial complementary information inherent in hyperspectral images.
S2ADet surpasses existing state-of-the-art methods, achieving robust and reliable results.
arXiv Detail & Related papers (2023-06-14T09:01:50Z) - Semi-Automated Segmentation of Geoscientific Data Using Superpixels [4.035753155957697]
Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy.
Inspired by the concept of superpixels, we propose a deep-learning based approach to segmentized survey data into regions with similar characteristics.
arXiv Detail & Related papers (2023-03-20T19:21:46Z) - Unsupervised ore/waste classification on open-cut mine faces using
close-range hyperspectral data [1.8111829286068908]
A pipeline for unsupervised mapping of spectra on a mine face is proposed.
The proposed pipeline brings together unsupervised and self-supervised algorithms in a unified system to map minerals on a mine face.
The consistency of its mapping capability is demonstrated using data acquired at two different times of day.
arXiv Detail & Related papers (2023-02-09T21:03:03Z) - Probabilistic Deep Metric Learning for Hyperspectral Image
Classification [91.5747859691553]
This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification.
It aims to predict the category of each pixel for an image captured by hyperspectral sensors.
Our framework can be readily applied to existing hyperspectral image classification methods.
arXiv Detail & Related papers (2022-11-15T17:57:12Z) - Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet
Transmission Spectra [68.8204255655161]
We focus on unsupervised techniques for analyzing spectral data from transiting exoplanets.
We show that there is a high degree of correlation in the spectral data, which calls for appropriate low-dimensional representations.
We uncover interesting structures in the principal component basis, namely, well-defined branches corresponding to different chemical regimes.
arXiv Detail & Related papers (2022-01-07T22:26:33Z) - Hyperspectral Image Segmentation based on Graph Processing over
Multilayer Networks [51.15952040322895]
One important task of hyperspectral image (HSI) processing is the extraction of spectral-spatial features.
We propose several approaches to HSI segmentation based on M-GSP feature extraction.
Our experimental results demonstrate the strength of M-GSP in HSI processing and spectral-spatial information extraction.
arXiv Detail & Related papers (2021-11-29T23:28:18Z) - Spectral Analysis Network for Deep Representation Learning and Image
Clustering [53.415803942270685]
This paper proposes a new network structure for unsupervised deep representation learning based on spectral analysis.
It can identify the local similarities among images in patch level and thus more robust against occlusion.
It can learn more clustering-friendly representations and is capable to reveal the deep correlations among data samples.
arXiv Detail & Related papers (2020-09-11T05:07:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.