Analyzing Urban Area Land Coverage Using Image Classification Algorithms

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)

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.

Keywords

Decision tree Feature extraction Image classification K nearest neighbors Naïve bayes Urban land cover 

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Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.PSG College of Arts and ScienceCoimbatoreIndia

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