Classification of rock textures

Abstract

This paper presents a novel method for the classification of rocks into the three major categories, namely, igneous, sedimentary and metamorphic. Each of these rock types has various sub-types too. The various Tamura Features are formulated and calculated from the input image. The values obtained are compared with the query image by Sum of Squared Distance (SSD. The classified results are then compared with those results of Grey Level Cooccurance Matrix (GLCM), Color cooccurance matrix and Moments. The proposed method outperforms the other previously developed methods by providing the classification accuracy of more than 87% for all the three types of rocks. The proposed method significantly improves efficiency with less computational complexity. © 2012 Springer-Verlag GmbH Berlin Heidelberg.