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

@article{Stavrakoudis2010EnhancingTI,
  title={Enhancing the interpretability of genetic fuzzy classifiers in land cover classification from hyperspectral satellite imagery},
  author={Dimitris G. Stavrakoudis and Georgia N. Galidaki and Ioannis Z. Gitas and Ioannis B. Theocharis},
  journal={International Conference on Fuzzy Systems},
  year={2010},
  pages={1-8}
}
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… CONTINUE READING