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Unsupervised textural classification of rocks in large imagery datasets
Citation
Merrill-Cifuentes, J and Cracknell, MJ and Escolme, A, Unsupervised textural classification of rocks in large imagery datasets, Minerals Engineering, 180 Article 107496. ISSN 0892-6875 (2022) [Refereed Article]
Copyright Statement
© 2022 Elsevier Ltd. All rights reserved.
DOI: doi:10.1016/j.mineng.2022.107496
Abstract
The value of rock characterisation, whether it is for mineral exploration, extraction, or concentration, lies in the ability to describe its composition and texture. In the past century, extensive technological developments have provided new opportunities to assess compositional properties, both geochemical and mineralogical, and at different scales: X-ray diffraction, X-ray fluorescence, inductively coupled plasma mass spectrometry, reflectance spectroscopy, etc. More recently, the advent of imaging characterisation techniques and high-performance computing power has enabled the assessment of mineral texture in a robust and quantitative manner. This study proposes and validates an end-user focused workflow for the identification of textural families in a large drill-core hyperspectral imagery dataset, based on a novel textural feature extraction method named Mineral Co-Occurrence Probability Field (MCOPF). This workflow combines vintage image textural assessment methods with modern machine learning techniques for the automated unsupervised classification of textures within a drill core hyperspectral imagery dataset. The results demonstrate a meaningful and robust identification of rock textural families (clusters), enabling a wide range of applications in geology, mining, and metallurgy in the academic and industrial sectors.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | Texture; Classification; Hyperspectral; Machine learning; Clustering; Geology; Characterisation; Geometallurgy |
| Research Division: | Information and Computing Sciences |
| Research Group: | Computer vision and multimedia computation |
| Research Field: | Image processing |
| Objective Division: | Mineral Resources (Excl. Energy Resources) |
| Objective Group: | Primary mining and extraction of minerals |
| Objective Field: | Mining and extraction of copper ores |
| UTAS Author: | Merrill-Cifuentes, J (Mr Javier Merrill Cifuentes) |
| UTAS Author: | Cracknell, MJ (Dr Matthew Cracknell) |
| UTAS Author: | Escolme, A (Dr Angela Escolme) |
| ID Code: | 153045 |
| Year Published: | 2022 |
| Deposited By: | CODES ARC |
| Deposited On: | 2022-09-02 |
| Last Modified: | 2022-11-17 |
| Downloads: | 0 |
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