Enhancing the interpretability of genetic fuzzy classifiers in land cover classification from hyperspectral satellite imagery

A Feature Selective Linguistic Classifier (FeSLiC) is proposed in this paper, for land cover classification from hyperspectral images. FeSLiC is a Genetic Fuzzy Rule-Based Classification System (GFRBCS), designed under the Iterative Rule Learning (IRL) approach. A local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevancy of each feature with respect to its classification ability. A simplification post-processing stage significantly enhances the interpretability of the derived model, by reducing its structure size. The performance of the classifier is finally optimized through a genetic tuning stage. Comparative results using an Earth Observing-1 (EO-1) Hyperion satellite image indicate the effectiveness of the proposed methodology in handling high-dimensional feature spaces.