Hindawi Publishing Corporation Mathematical Problems in Engineering Volume 2010, Article ID 163635, 23 pages doi:10.1155/2010/163635 Research Article Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method Laercio B. Gonc ¸ alves 1, 2 and Fabiana R. Leta 1 1 Computational and Dimensional Metrology Laboratory (LMDC), Mechanical Engineering Department (PGMEC), Universidade Federal Fluminense (UFF), R. Passo da P´ atria, 156, Niter´ oi, Rio de Janeiro, 24210-240, Brazil 2 Computer Department, Centro Federal de Educac ¸˜ ao Tecnol´ ogica Celso Suckow da Fonseca (CEFET-RJ), Av. Maracan˜ a, 229, Rio de Janeiro, 20271-110, Brazil Correspondence should be addressed to Fabiana R. Leta, fabiana@ic.uff.br Received 6 October 2009; Revised 22 April 2010; Accepted 17 May 2010 Academic Editor: Panos Liatsis Copyright q 2010 L. B. Gonc ¸alves and F. R. Leta. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We used a Hierarchical Neuro-Fuzzy Class Method based on binary space partitioning NFHB- Class Method for macroscopic rock texture classification. The relevance of this study is in helping Geologists in the diagnosis and planning of oil reservoir exploration. The proposed method is capable of generating its own decision structure, with automatic extraction of fuzzy rules. These rules are linguistically interpretable, thus explaining the obtained data structure. The presented image classification for macroscopic rocks is based on texture descriptors, such as spatial variation coefficient, Hurst coefficient, entropy, and cooccurrence matrix. Four rock classes have been evaluated by the NFHB-Class Method: gneiss two subclasses, basalt four subclasses, diabase five subclasses, and rhyolite five subclasses. These four rock classes are of great interest in the evaluation of oil boreholes, which is considered a complex task by geologists. We present a computer method to solve this problem. In order to evaluate system performance, we used 50 RGB images for each rock classes and subclasses, thus producing a total of 800 images. For all rock classes, the NFHB-Class Method achieved a percentage of correct hits over 73%. The proposed method converged for all tests presented in the case study. 1. Introduction Oil is an essential energy resource for industrial production. It can be found in a variety of geological environments. The exploitation of oil is a large-scale activity, in which the acquisition, distribution and use of expert knowledge are critical to decision making. Two sets of data are of fundamental importance in the exploitation of a new oilfield: the oil reservoir geometry and the description of the type of porous rock that holds the oil. When analyzing the oil reservoir geometry, it is possible to identify the amount of oil in the