High Resolution Data Processing for Spatial Image Data Mining

This paper contributes towards the development of adaptive learning system for automated segmentation and prediction of isolated regions in given spatial images. The effect of spatial distortion is observed in the spatial images under different processing noise conditions. A method for image denoising, shape and textural feature information using multi wavelet transformation is suggested. The regions in the image are estimated using global graph theory technique. A methodology to provide guidance for mining Remote sensing image data is proposed. To improve the accuracy of estimation, hierarchal clustering over distributed data sample is presented. The concepts of linear relation among various clusters are explored and are incorporated in data mining approach. The performance of retrieval time and classification accuracy has been evaluated for various cases.