Resumo
In this work we present a method to classify a set of rock textures based on a Spectral Analysis and the extraction of the texture Features of the resulted images. Up to 520 features were tested using 4 different filters and all $31$ different combinations were verified. The classification process relies on a Na\"{i}ve Bayes classifier. We performed two kinds of optimizations: statistical optimization with covariance-based Principal Component Analysis (PCA) and a genetic optimization, for 10,000 randomly defined samples, achieving a final maximum classification success of 91\% against the original $\sim$ 70\% success ratio (without any optimization nor filters used). After the optimization $9$ types of features emerged as most relevant