VISUAL DATA MINING IN LULC SATELLITE IMAGERY

Abstract

This study presents a new visualization tool for segmentation, classification, clustering and 3D space feature plot of clustered classes of high resolution LUCL satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery into volume visualization. This volume based representation divides feature space into cubes or voxels. The visualization tool is showcased in a classification study of high-resolution imageries of Latur district in Maharashtra state of India. In this research paper a new visualization tool for classification of LULC satellite imagery is designed. The tool can be used to calculate the covered area, pixels frequencies, pixels color combinations etc. of different pattern classes of LULC and displays the pattern classes in 3D spherical view according to their calculated sizes and color.