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Analyzing Urban Area Land Coverage Using Image Classification Algorithms

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Computational Intelligence in Data Mining - Volume 2

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

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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.

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References

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. Photogramm. Remote Sens. 65, 2–16 (2010)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Lillesand, T.M., Kiefer, R.W., Chipman, W.: Remote Sensing and Image Interpretation. Wiley, New York (2008)

    Google Scholar 

  8. Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Sys. Man Cyber. 3, 610–621 (1973)

    Article  Google Scholar 

  9. Zhang, D., Lu, G.: Review of shape representation and description techniques. Pattern Recognit. 37, 1–19 (2004)

    Article  MATH  Google Scholar 

  10. Peura, M., Iivarinen, J.: Efficiency of simple shape descriptors. Asp. Vis. Form. 443–451 (1997)

    Google Scholar 

  11. Langley, P.: Selection of relevant features in machine learning. In: AAAI Fall Symposium, pp. 127–131 (1994)

    Google Scholar 

  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. Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools And Techniques. Elsevier, Amsterdam (2011)

    Google Scholar 

  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. 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. Karthikeyan, T., Thangaraju, P.: Analysis of classification algorithms applied to hepatitis patients. Int. J. Comp. Applns. 62(5), 25–30 (2013)

    Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  19. Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann, Burlington (1993)

    Google Scholar 

  20. Breiman, L., Cutler, A.: Random forests. Available online: http://www.stat.berkeley.edu/users/breiman/RandomForests. Accessed on 4 June 2014

  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)

    Article  Google Scholar 

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Authors and Affiliations

  1. PSG College of Arts and Science, Coimbatore, Tamil Nadu, India

    T. Karthikeyan & P. Manikandaprabhu

Authors
  1. T. Karthikeyan
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  2. P. Manikandaprabhu
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Corresponding author

Correspondence to T. Karthikeyan .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Karthikeyan, T., Manikandaprabhu, P. (2015). Analyzing Urban Area Land Coverage Using Image Classification Algorithms. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_40

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  • DOI: https://doi.org/10.1007/978-81-322-2208-8_40

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2207-1

  • Online ISBN: 978-81-322-2208-8

  • eBook Packages: EngineeringEngineering (R0)

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