A Novel Approach for Satellite Imagery Storage by Classifying the Non-duplicate Regions

Everyday satellite is capturing thousands of images which needs to be classified in a proper way. In this paper, we address the problem of replacing the existing images with the captured one. We provide a new solution by storing only the non-existing part of the image. Though satellite images have been classified in past by using various techniques, the researchers are always finding alternative strategies for satellite image classification so that they may be prepared to select the most appropriate technique for the feature extraction task in hand. In order to overcome this difficulty, we propose an efficient approach, which consists of an algorithm that can adopt robust feature kernel principle component analysis (KPCA) to reduce dimensionality of image. Concerning image clustering, we utilize Fuzzy N-Means algorithm. Finally data is stored into 148 database according to specific class by utilizing support vector machine classifier. Thus the proposed scheme improve the efficient storage of satellite images in the database, save time consumption and make the correction of the satellite images more proficiently.