In this paper, the rock types of an iron ore deposit were classified using the digital image analysis technique. The image acquisition and analysis of blasted rocks were conducted in a laboratory for six different rock types. A total of 189 features were extracted from the individual rock samples using best-suited segmentation technique selected by validation study. The neural network technique was applied for rock classification model using image features. Five principal components, which accounts for 95% of total data variance, were selected as input parameters for the model. The misclassification error of the model for testing data was 2.4%.
Keywords: rock types, rock classification, image analysis, PCA, principal component analysis, PCA, neural networks, confusion matrix, iron ore deposits