Mathematical Problems in Engineering
Volume 2010 (2010), Article ID 163635, 23 pages
Research Article

Macroscopic Rock Texture Image Classification Using a Hierarchical Neuro-Fuzzy Class Method

1Computational and Dimensional Metrology Laboratory (LMDC), Mechanical Engineering Department (PGMEC), Universidade Federal Fluminense (UFF), R. Passo da Pátria, 156, Niterói, Rio de Janeiro, 24210-240, Brazil
2Computer Department, Centro Federal de Educação Tecnológica Celso Suckow da Fonseca (CEFET-RJ), Av. Maracanã, 229, Rio de Janeiro, 20271-110, Brazil

Received 6 October 2009; Revised 22 April 2010; Accepted 17 May 2010

Academic Editor: Panos Liatsis

Copyright © 2010 Laercio B. Gonçalves and Fabiana 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.