3D Visualization and Spatial Data Mining for Analysis of LULC Images

TL;DR

This paper introduces a novel 3D visualization tool combined with spatial data mining techniques for analyzing high-resolution LULC satellite images, enhancing pattern exploration and classification confidence.

Contribution

It presents a new prototype integrating image segmentation, K-Means clustering, and 3D volume visualization for improved analysis of land cover data.

Findings

Effective visualization of feature space patterns.

Enhanced understanding of classification uncertainty.

Successful application to high-resolution satellite imagery.

Abstract

The present study is an attempt made to create a new tool for the analysis of Land Use Land Cover (LUCL) images in 3D visualization. This study mainly uses spatial data mining techniques on high resolution LULC 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 and understanding of the results. In this study, we present a prototype of image segmentation, K-Means clustering and 3D visualization tool for visual data mining (VDM) of LUCL satellite imagery into volume visualization. This volume based representation divides feature space into spheres or voxels. The visualization tool is showcased in a classification study of high-resolution LULC imagery of Latur district (Maharashtra state, India) is used as sample data.