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Unsupervised textural classification of rocks in large imagery datasets


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


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

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