Analyzing Urban Area Land Coverage Using Image Classification Algorithms

  • T. Karthikeyan
  • P. Manikandaprabhu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 32)


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.


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


  1. 1.
    Peddle, D.R., Ferguson, D.T.: Optimization of multisource data analysis: an example using evidence reasoning for GIS data classification. Comp. Geosci 28, 45–52 (2002)CrossRefGoogle Scholar
  2. 2.
    Li, G., Lu, D., Moran, E., Hetrick, S.: Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery. Int. J. Remote Sens. 32(23), 8207–8230 (2011)CrossRefGoogle Scholar
  3. 3.
    Karthikeyan, T., Manikandaprabhu, P., Nithya, S.: A survey on text and content based image retrieval system for image mining. Int. J. Engg. Res. Tech. 3(3), 509–512 (2014)Google Scholar
  4. 4.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16 (2010)CrossRefGoogle Scholar
  5. 5.
    Schneider, A.: Monitoring land cover change in urban and peri-urban areas using dense time stacks of Landsat satellite data and a data mining approach. Remote Sens. Environ. 124, 689–704 (2012)CrossRefGoogle Scholar
  6. 6.
    Zhang, Q., Wang, J., Peng, X., Gong, P., Shi, P.: Urban built-up land change detection with road density and spectral information from multi-temporal Landsat TM data. Int. J. Remote Sens. 23, 3057–3078 (2002)CrossRefGoogle Scholar
  7. 7.
    Lillesand, T.M., Kiefer, R.W., Chipman, W.: Remote Sensing and Image Interpretation. Wiley, New York (2008)Google Scholar
  8. 8.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Sys. Man Cyber. 3, 610–621 (1973)CrossRefGoogle Scholar
  9. 9.
    Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognit. 37, 1–19 (2004)CrossRefMATHGoogle Scholar
  10. 10.
    Peura, M., Iivarinen, J.: Efficiency of simple shape descriptors. Asp. Vis. Form. 443–451 (1997)Google Scholar
  11. 11.
    Langley, P.: Selection of relevant features in machine learning. In: AAAI Fall Symposium, pp. 127–131 (1994)Google Scholar
  12. 12.
    Koprinska, I.: Feature selection for brain-computer interfaces. In: 13th Pacific-Asia Internatioanl Conference on Knowledge Discovery and Data Mining: New Frontiers in Applied Data Mining, pp. 106–117 (2009)Google Scholar
  13. 13.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools And Techniques. Elsevier, Amsterdam (2011)Google Scholar
  14. 14.
    Karthikeyan, T., Thangaraju, P.: PCA-NB algorithm to enhance the predictive accuracy. Int. J. Eng. Tech. 6(1), 381–387 (2014)Google Scholar
  15. 15.
    Dumitru, D.: Prediction of recurrent events in breast cancer using the Naive Bayesian classification. Ann. Univ. Craiova Math. Comp. Sci. Ser. 36(2) (2009)Google Scholar
  16. 16.
    Karthikeyan, T., Thangaraju, P.: Analysis of classification algorithms applied to hepatitis patients. Int. J. Comp. Applns. 62(5), 25–30 (2013)Google Scholar
  17. 17.
    Wu, X., Kumar, V., Quinlan, J.R., Ghosh, J., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14, 1–37 (2007)CrossRefGoogle Scholar
  18. 18.
    Wieland, M., Pittore, M.: Performance evaluation of machine learning algorithms for urban pattern recognition from multi-spectral satellite images. Remote Sens. 6, 2912–2939 (2014)CrossRefGoogle Scholar
  19. 19.
    Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann, Burlington (1993)Google Scholar
  20. 20.
    Breiman, L., Cutler, A.: Random forests. Available online: Accessed on 4 June 2014
  21. 21.
    Pal, M., Mather, P.M.: An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sens. Environ. 86, 554–565 (2003)CrossRefGoogle Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.PSG College of Arts and ScienceCoimbatoreIndia

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