Scientific Quarterly Journal of Geosciences

Scientific Quarterly Journal of Geosciences

Enhancing lithological mapping using pixel- and object-based machine learning on Sentinel-2 and PRISMA Data: A case study from the Remeshk-Mokhtarabad ophiolite complex

Document Type : Original Research Paper

Authors
1 Department of Geology, College of Sciences, Shahid Bahonar University of Kerman, Kerman, Iran
2 Geological Survey of Iran, Tehran, Iran
10.22071/gsj.2025.551653.2227
Abstract
Machine learning algorithms and lithological interpretation based on remote sensing play a crucial role in regional geological studies; however, expert interpretation by experienced geologists remains irreplaceable. This study employs multispectral Sentinel-2 and hyperspectral PRISMA satellite data to evaluate lithological mapping of the Remeshk–Mokhtarabad ophiolitic complex located in the northern Makran, southeastern Iran. The research focuses on integrating remote sensing technologies with machine learning algorithms to enhance geological mapping accuracy and to support field-based investigations. Selection of optimal input features for classifiers is considered a key objective of this study. To this end, various image enhancement techniques, including spectral analysis, band combinations and ratios, principal component analysis, color ratio composites, and minimum noise fraction transformation, were applied. Subsequently, machine learning algorithms, specifically neural networks, support vector machines, and k-nearest neighbors were applied for classification. Accuracy assessment based on overall accuracy and the kappa coefficient indicates that the object-based approach applied to multispectral Sentinel-2 imagery produces more homogeneous maps with higher accuracy, whereas the pixel-based approach yields better performance for hyperspectral PRISMA data. The results demonstrate that the combined use of Sentinel-2 and PRISMA data provides a powerful tool for lithological mapping in ophiolitic complexes.
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