Object Classification of Satellite Images Using Cluster Repulsion Based Kernel Fcm and Svm Classifier

We investigated the Classification of satellite images and multispectral remote sensing data .we focused on uncertainty analysis in the produced land-cover maps .we proposed an efficient technique for classifying the multispectral satellite images using Support Vector Machine (SVM) into road area, building area and green area. We carried out classification in three modules namely (a) Preprocessing using Gaussian filtering and conversion from conversion of RGB to Lab color space image (b) object segmentation using proposed Cluster repulsion based kernel Fuzzy CMeans (FCM) and (c) classification using one-to-many SVM classifier. The goal of this research is to provide the efficiency in classification of satellite images using the object-based image analysis. The proposed work is evaluated using the satellite images and the accuracy of the proposed work is compared to FCM based classification. The results showed that the proposed technique has achieved better results reaching an accuracy of 79%, 84%, 81% and 97.9% for road, tree, building and vehicle classification respectively.