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Image Classification is the process of assigning classes to the pixels in remote sensed images and important for GIS applications, since the classified image is much easier to incorporate than the original unclassified image. To resolve misclassification in traditional parametric classifier like Maximum Likelihood, the object oriented techniques offer suitable parameters in some level to classify the satellite data. To build knowledge base automatically, this paper explores a non-parametric decision tree classifier to extract knowledge from the spatial data in the form of classification rules. A new method is proposed using a data structure called Peano Count Tree (P-tree) for decision tree classification. The accuracy is Passessed using the parameters overall accuracy, User's accuracy and Producer's accuracy for image classification methods of object oriented classification, Knowledge Base Classification, Post classification and P-tree Classifier. The results reveal that the knowledge extracted from decision tree classifier and P-tree data structure from proposed approach remove the problem of spectral confusion to a greater extent. It is ascertained that the P-tree classifier is surpasses the other classification techniques.