Land cover classification using reformed fuzzy C-means

This paper explains the task of land cover classification using reformed fuzzy C means. Clustering is the assignment of objects into groups called clusters so that objects from the same cluster are more similar to each other than objects from different clusters. The most basic attribute for clustering of an image is its luminance amplitude for a monochrome image and colour components for a colour image. Since there are more than 16 million colours available in any given image and it is difficult to analyse the image on all of its colours, the likely colours are grouped together by clustering techniques. For that purpose reformed fuzzy C means algorithm has been used. The segmented images are compared using image quality metrics. The image quality metrics used are peak signal to noise ratio (PSNR), error image and compression ratio. The time taken for image segmentation is also used as a comparison parameter. The techniques have been applied to classify the land cover.