Author(s)

Wognin Joseph Vangah¹, Bi G. Théodore Toa², Alico Nango Jerôme³, Ouattara Sie⁴, Alain Clément⁵

¹Unité de Recherche et d’expertise Numérique (UREN), Université Virtuelle de Côte d’Ivoire (UVCI), Abidjan, Côte d’Ivoire.
²UFR Sciences et technologique, Département de Physique, Université polytechnique de Man (UPM), Man, Côte d’Ivoire.
³Classe Préparatoire des Grandes Ecoles (CPGE), Université de San Pédro (USP), San Pédro, Côte d’Ivoire.
⁴Institut National Polytechnique Houphouët (INPHB), Yamoussoukro, Côte d’Ivoire.
⁵Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Institut Universitaire de Technologie (IUT), Université d’Angers, Angers, France.

In geology, classification and lithological recognition of rocks plays an important role in the area of oil and gas exploration, mineral exploration and geological analysis. In other fields of activity such as construction and decoration, this classification makes sense and fully plays its role. However, this classification is slow, approximate and subjective. Automatic classification curbs this subjectivity and fills this gap by offering methods that reflect human perception. We propose a new approach to rock classification based on direct-view images of rocks. The aim is to take advantage of feature extraction methods to estimate a rock dictionary. In this work, we have developed a classification method obtained by concatenating four (4) K-SVD variants into a single signature. This method is based on the K-SVD algorithm combined with four (4) feature extraction techniques: DCT, Gabor filters, D-ALBPCSF and G-ALBPCSF, resulting in the four (4) variants named K-Gabor, K-DCT, KD-ALBPCSF and KD-ALBPCSF respectively. In this work, we developed a classification method obtained by concatenating four (4) variants of K-SVD. The performance of our method was evaluated on the basis of performance indicators such as accuracy with other 96% success rate.

Rock Recognition, Dictionary, Signature, Color Texture, K-SVD Variants, KD-ALBPCSF Et KG-ALBPCSF

Vangah, W. , Toa, B. , Jerôme, A. , Sie, O. and Clément, A. (2024) New Approach to Rock Classification Based on Sparse Representations. Open Journal of Applied Sciences, 14, 145-158. doi: 10.4236/ojapps.2024.141011.