Authors: Snehamoy Chatterjee, Ashis Bhattacherjee, Biswajit Samanta, Samir Kumar Pal
Addresses: Department of Mining Engineering, National Institute of Technology Rourkela, Orissa 769008, India. ' Department of Mining Engineering, IIT Kharagpur, Kharagpur – 721302, West Bengal, India. ' Department of Mining Engineering, IIT Kharagpur, Kharagpur – 721302, West Bengal, India. ' Department of Mining Engineering, IIT Kharagpur, Kharagpur – 721302, West Bengal, India
Abstract: 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.
International Journal of Mining and Mineral Engineering, 2008 Vol.1 No.1, pp.22 - 46
Published online: 27 Sep 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article