Analyzing urban area land coverage using image classification algorithms

Abstract

In this paper mainly deals with classifying high resolution image of an urban land cover area. It aims to extract the features like texture, shape, size and spectral information in feature extraction process. In this work, various classification algorithms particularly Naïve Bayes, IBk, J48 and Random Tree are implemented. The classification accuracy always depends on the effectiveness of the extracted features. Experimental results show that the accuracy performance obtained by Decision Tree based J48 algorithm is better than other classification algorithms.