Discernment of complex lithologies utilizing different scattering and textural components of SAR and optical data through machine learning approaches in Jaisalmer, Rajasthan, India

Accurate lithological mapping is a difficult task through standard image processing techniques. We utilize the application of different machine learning (ML) algorithms on dual polarimetric synthetic aperture radar (SAR), optical data, and surface elevation images to map various lithologies in parts of Jaisalmer district of Rajasthan, India. Different SAR-derived textural and decomposition parameters were also used to improve the discrimination of various lithology units. Further, to improve the classification accuracy, different ML-based feature importance models, such as XGboost, decision tree, and random forest were implemented to select the useful bands for the classification of lithology. A total of 14 different ML classifiers were applied, and the best classifier was chosen after comparing their accuracies (overall accuracy, kappa coefficient, F1 score, and ROC-AUC curve) to map the lithology. Out of all of the classifiers used in this study, light gradient boosting machine (lightgbm) performed better in discriminating lithology (OA = 0.80, kappa coefficient = 0.75, and F1 score 0.79). In addition, the AUC values (>0.9 in all lithology units) were obtained for the "lightgbm" model, which is indicative of accurate discrimination of different lithological classes.