MSIMRS: Multi-Scale Superpixel Segmentation Integrating Multi-Source Remote Sensing Data for Lithology Identification in Semi-Arid Area

Lithology classification stands as a pivotal research domain within geological Remote Sensing (RS). In recent years, extracting lithology information from multi-source RS data has become an inevitable trend. Various classification image primitives yield distinct outcomes in lithology classification. The current research on lithology classification utilizing RS data has predominantly concentrated on pixel-level classification, which suffers from a long classification time and high sensitivity to noise. In order to explore the application potential of superpixel segmentation in lithology classification, this study proposed the Multi-scale superpixel Segmentation Integrating Multi-source RS data (MSIMRS), and conducted a lithology classification study in Duolun County, Inner Mongolia Autonomous Region, China combining MSIMRS and the Support Vector Machine (MSIMRS-SVM). In addition, pixel-level K-Nearest Neighbor (KNN), Random Forest (RF) and SVM classification algorithms, as well as deep-learning models including Resnet50 (Res50), Efficientnet_B8 (Effi_B8), and Vision Transformer (ViT) were chosen for a comparative analysis. Among these methods, our proposed MSIMRS-SVM achieved the highest accuracy in lithology classification in a typical semi-arid area, Duolun County, with an overall accuracy and Kappa coefficient of 92.9% and 0.92. Moreover, the findings indicate that incorporating superpixel segmentation into lithology classification resulted in notably fewer fragmented patches and significantly improved the visualization effect. The results showcase the application potential of superpixel primitives in lithology information extraction within semi-arid areas.