A Fast Iterative Rule-based Linguistic Classifier for hyperspectral remote sensing tasks

Abstract

This paper introduces a genetic fuzzy rule-based classification system (GFRBCS), specifically designed to effectively handle highly-dimensional features spaces. The proposed methodology follows the principles of the iterative rule learning (IRL) approach, whereby a rule extraction algorithm (REA) is invoked in an iterative fashion, producing one fuzzy rule at a time. The REA is performed in two successive steps: the first one selects the relevant features of the currently extracted rule, whereas the second one decides the antecedent part of the fuzzy rule, using the previously selected subset of features. The performance of the classifier is finally optimized through a genetic tuning post-processing stage. Comparative results using a hyperspectral satellite image indicate the effectiveness of the proposed methodology in handling highly-dimensional classification problems, compared to other GFRBCSs.