A Fuzzy Rule-Based Feature Construction Approach Applied to Remotely Sensed Imagery

The inherent interpretability properties of fuzzy rule-based classification systems (FRBCSs) are undoubtedly one of their major advantages when compared to conventional black-box classifiers. In this paper we present a preliminary study of how the so-called technique of feature construction can prove useful in the context of land cover classification tasks using remotely sensed imagery. The method is integrated into a previously proposed genetic FRBCS (GFRBCS) and applied in a crop classification task using a multispectral satellite image. The experimental analysis shows that feature construction can effectively identify very useful hidden relationships among the initial variables of the problem.