Vision-based rock-type classification of limestone using multi-class support vector machine

Rock-type classification is a challenging and difficult job due to the heterogeneous properties of rocks. In this paper, an image-based rock-type analysis and classification method is proposed. The study was conducted at a limestone mine in western India using stratified random sampling from a case study mine. The analysis of collected sample images was performed in laboratory. Color, morphology, and textural features were extracted from the captured image and a total of 189 features were recorded. The multi-class support vector machine (SVM) algorithm was then applied for rock-type classification. The hyper-parameters and the number of input features of the SVM model were selected by genetic algorithm. The results revealed that the SVM model performed best when 40 features were selected out of the 189 extracted features. The results demonstrated that the overall accuracy of the proposed technique for rock type classification is 96.2 %. A comparative study shows that the proposed SVM model performed better than a competing neural network model in this case study mine.