Multispectral data classification based on spectral indices and cascaded fuzzy C-mean classifiers

Land Use and Land Cover (LULC) are characterized by a large variety of spectrally distinct LULC classes. The diagnostic and evaluation of the spectral separability measure yields the potential for automated identification and mapping of these classes. This study proposes a new cascaded fuzzy C-mean classification method for a rough classification of LULC classes. The method is based on the use of spectral indices as innovative features to provide a coarse classification of remotely sensed data. The robustness and accuracy of the defined classification schema is performed based on the computation of confusion matrices and Kappa coefficient. The Kappa statistic ranges from 0.90 to 0.98 for the set of the evaluated images which infer the good accuracy of the new rough classification scheme.