Jafari, Laleh, Sanislav, Ioan V., Jarihani, Ben, Duce, Stephanie, and Koci, Jack (2026) Machine Learning- and Remote Sensing-Based Lithological Mapping Using VNIR + SWIR PRISMA Hyperspectral and ASTER Multispectral Datasets in Northwest of Queensland. Minerals, 16 (7). 720.

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Abstract

Lithological mapping is essential for geological studies, mineral exploration, and environmental assessment. Satellite remote sensing combined with machine learning provides a scalable, cost-effective approach for regional lithological discrimination. This study evaluates hyperspectral and multispectral satellite imagery for lithological mapping in a geologically complex region of northwestern Queensland, Australia. The study area, within the Mount Isa Inlier, comprises diverse sedimentary, volcanic, intrusive, and metamorphic lithologies. PRISMA hyperspectral and ASTER multispectral imagery were analyzed using supervised classification algorithms, including Support Vector Machine (SVM), Mahalanobis Distance (MaDC), Minimum Distance (MDC), and Maximum Likelihood (MLC). Image-derived endmembers from representative lithologies were used as training data. Classification accuracy was assessed using confusion matrices, Overall Accuracy (OA), and the Kappa coefficient. PRISMA imagery outperformed ASTER data. SVM achieved the highest performance for PRISMA (OA = 82.03%, Kappa = 0.81), whereas MLC achieved the highest performance for ASTER (OA = 33.29%, Kappa = 0.30). Classification accuracy was evaluated using an independent set of validation ROIs that were spatially separated from the training samples, providing a more reliable estimate of model performance. These results highlight the benefits of hyperspectral remote sensing with machine learning for lithological discrimination in complex terrain and emphasise the importance of spatially independent validation. The approach demonstrates strong potential for regional-scale applications and may support more efficient mineral exploration and geological mapping workflows.

Item ID:	92613
Item Type:	Article (Research - C1)
ISSN:	2075-163X
Copyright Information:	© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Date Deposited:	29 Jul 2026 06:50
FoR Codes:	37 EARTH SCIENCES > 3704 Geoinformatics > 370402 Earth and space science informatics @ 50% 37 EARTH SCIENCES > 3705 Geology > 370508 Resource geoscience @ 30% 37 EARTH SCIENCES > 3705 Geology > 370503 Igneous and metamorphic petrology @ 20%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280107 Expanding knowledge in the earth sciences @ 100%
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