Machine learning-based lithological mapping from ASTER remote-sensing imagery in the Sivas basin with complex due to salt tectonics

This study presents a comparative analysis of several machine learning (ML) algorithms for lithological mapping of the tectonically complex geology of the Sivas Basin, Türkiye, where salt tectonics and halokinetic deformation make spectral-based separations particularly challenging. Five target classes, including gypsum, limestone, the Karacaören Formation (Tkk), the Karayün Formation (Tsz), and vegetation were distinguished from a dataset of 1,744 endmember samples derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) VNIR and SWIR bands. The five ML algorithms examined were the J48 decision tree (WEKA), Fine Tree, Support Vector Machine (SVM), k-Nearest Neighbor (KNN), and a Wide Neural Network (WideNN). The results indicate the strong potential of advanced classifiers for lithological prediction, with both WideNN and KNN achieving similar performance, exhibiting overall accuracies of 0.997. SVM also demonstrated strong generalization capability, even for high-dimensional spectral data. Feature importance analysis showed that the most influential bands were VNIR bands (B1 and B2) and SWIR bands (B4, B5 and B9), while the superior performance of WideNN and SVM was attributed to their ability to exploit additional spectral information from B7 and B8. These capabilities enabled a clear distinction between spectrally similar units such as Tkk and Tsz. Field validation based on 300 checkpoints confirmed the accuracy of the ML-based predictions, with SVM and KNN yielding the highest agreement with field observations. Overall, the results highlight the strong potential of integrating ML techniques with multispectral satellite imagery to improve lithological mapping in salt-tectonic basins and other geologically complex terrains.