Land cover classification using interval type-2 fuzzy clustering for multi-spectral satellite imagery

Land cover classification have been developed for specially surveillance of change of land and generating update information. The paper introduces an approach to classification of land cover from multi-spectral satellite imagery using interval type-2 c-means clustering. Two channels (Near Infrared - NIR and Visible Red - NR) are used to generate NDVI image of study area. Then IT2-FCM is used to classify NDVI into six sub-classes presenting for six types of land cover. The method is implemented for two study areas in comparing with ISODATA algorithm and FCM.