High-Resolution Lithology Detection Using Sentinel-2A, ALOS PRISM L1B Images, and Support-Vector Machines in Tagragra d’Akka Inlier of Western Anti-Atlas, Morocco

Abstract

Geological mapping faces substantial challenges due to inaccessible terrains, labor-intensive field methods, and potential interpretative errors. This study proposes an innovative approach that leverages automatic lithology classification using multispectral Sentinel-2A (10 m) and high-resolution panchromatic ALOS PRISM L1B (2.5 m) images. Applied to the Tagragra d’Akka inlier of the Anti-Atlas region, the methodology enhances spatial resolution through pansharpening, followed by unsupervised segmentation. The segmented images are classified using support vector machines (SVMs) (supervised learning algorithms) to distinguish the lithological units. Achieving an 86% overall accuracy and an 84% kappa coefficient, the approach demonstrated robust performance and surpassed conventional techniques. The integration of machine learning and remote sensing offers a promising frontier for geological mapping – particularly in regions like the Tagragra d’Akka inlier. This study marks a significant advancement in automating lithological mapping, with implications for geological research, resource management, and hazard assessment. Automated techniques in geological cartography significantly enhance mapping accuracy and efficiency. Future studies should explore additional data sources and machine-learning algorithms to refine lithological classification and validate these methods across diverse geological settings.