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Support Vector Machine Classification for Object-Based Image Analysis

  • A. Tzotsos
  • D. Argialas
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)

Abstract

The Support Vector Machine is a theoretically superior machine learning methodology with great results in pattern recognition. Especially for supervised classification of high-dimensional datasets and has been found competitive with the best machine learning algorithms. In the past, SVMs were tested and evaluated only as pixel-based image classifiers. During recent years, advances in Remote Sensing occurred in the field of Object-Based Image Analysis (OBIA) with combination of low level and high level computer vision techniques. Moving from pixel-based techniques towards object-based representation, the dimensions of remote sensing imagery feature space increases significantly. This results to increased complexity of the classification process, and causes problems to traditional classification schemes. The objective of this study was to evaluate SVMs for their effectiveness and prospects for object-based image analysis as a modern computational intelligence method. Here, an SVM approach for multi-class classification was followed, based on primitive image objects provided by a multi-resolution segmentation algorithm. Then, a feature selection step took place in order to provide the features for classification which involved spectral, texture and shape information. After the feature selection step, a module that integrated an SVM classifier and the segmentation algorithm was developed in C++. For training the SVM, sample image objects derived from the segmentation procedure were used. The proposed classification procedure followed, resulting in the final object classification. The classification results were compared to the Nearest Neighbor object-based classifier results, and were found satisfactory. The SVM methodology seems very promising for Object Based Image Analysis and future work will focus on integrating SVM classifiers with rule-based classifiers.

Keywords

Machine Learning Computational Intelligence Multi-scale Segmentation Remote Sensing 

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References

  1. Argialas D. and Goudoula V. (2003). Knowledge-based Land Use Classification from IKONOS Imagery for Arkadi, Crete, Greece. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, Proceedings of SPIE Vol. 4886.Google Scholar
  2. Argialas D., and Harlow C. (1990). Computational Image Interpretation Models: An Overview and a Perspective. Photogrammetric Engineering and Remote Sensing, Vol. 56, No 6, June, pp. 871-886.Google Scholar
  3. Baatz M. and Schäpe A. (2000). Multiresolution Segmentation – an optimization approach for high quality multi-scale image segmentation. In: Strobl, J. et al. (eds.): Angewandte Geographische Infor-mationsverarbeitung XII. Wichmann, Heidelberg, pp. 12-23.Google Scholar
  4. Benz U., Hoffman P., Willhauck G., Lingenfelder I., Heynen M. (2004). Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS Journal of Photogrammetry and Remote Sensing 58 pp. 239-258.Google Scholar
  5. Biederman I. (1985). Human image understanding: Recent research and a theory. Computer Vision, Graphics, and Image Processing, 32, 29–73.CrossRefGoogle Scholar
  6. Brown M., Lewis H.G., Gunn S.R. (2000). Linear Spectral Mixture Models and Support Vector Machines for Remote Sensing. IEEE Transactions On Geoscience And Remote Sensing, Vol. 38, No. 5, September 2000Google Scholar
  7. Chang, C.-C. and Lin C.-J. (2001). LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~ cjlin/libsvm. (accessed 15/06/2006)Google Scholar
  8. Cortes C. and Vapnik V. (1995). Support-vector network. Machine Learning, 20:273–297, 1995.Google Scholar
  9. De Moraes R.M. (2004). An Analysis Of The Fuzzy Expert Systems Architecture for Multispectral Image Classification Using Mathematical Morphology Operators (Invited Paper) International Journal of Computational Cognition (http://www.YangSky.com/yangijcc.htm) Volume 2, Number 2, Pages 35–69, June 2004.Google Scholar
  10. eCognition User Guide, (2005). Definiens, Munchen. http://www.definiens.com (accessed 15/06/2006)Google Scholar
  11. Fang H. and Liang S. (2003). Retrieving Leaf Area Index With a Neural Network Method: Simulation and Validation IEEE Transactions On Geoscience And Remote Sensing, Vol. 41, No. 9, September 2003.Google Scholar
  12. Foody G.M. and Mathur A. (2004). A Relative Evaluation of Multiclass Image Classification by Support Vector Machines. IEEE Transactions On Geoscience And Remote Sensing, Vol. 42, No. 6, June 2004.Google Scholar
  13. Hsu C.-W. and Lin C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, vol. 13, pp. 415–425, Mar. 2002.CrossRefGoogle Scholar
  14. Huang C., Davis L. S., Townshend J. R. G. (2002). An assessement of support vector machines for land cover classifiocation,” Int. J. Remote sensing, vol. 23, no. 4, pp. 725–749, 2002.CrossRefGoogle Scholar
  15. Kanellopoulos I., Wilkinson G., Moons T. (1999). Machine Vision and Advanced Image Processing in Remote Sensing. Springer Verlag.Google Scholar
  16. Lillesand, T.M., Kiefer, R.W. (1987). Remote-Sensing and Image Interpretation. Wiley, New York.Google Scholar
  17. Melgani F. and Bruzzone L. (2004). Classification of Hyperspectral Remote Sensing Images With Support Vector Machines. IEEE Transactions On Geoscience And Remote Sensing, Vol. 42, No. 8, August 2004.Google Scholar
  18. Mercier G. and Lennon M. (2003). Support vector machines for hyperspectral image classification with spectral-based kernels. in Proc. IGARSS, Toulouse, France, July 21–25, 2003.Google Scholar
  19. Moller-Jensen L. (1997). Classification of Ubrban Land Cover Based on Expert Systems, Object Models and Texture. Comput. Environ and Urban Systems, Vol.21, No. 3/4, pp. 291-302, 1997.Google Scholar
  20. Negnevitsky M. (2005). Artificial Intelligence, a Guide to Intelligent Systems. Pearson Education, p.440, 2005.Google Scholar
  21. Sonka, M., Hlavac, V. Boyle, R. (1998). Image Processing, Analysis, and Machine Vision - 2nd Edition, PWS, Pacific Grove, CA, 800 p., ISBN 0-534-95393-X.Google Scholar
  22. Theodoridis S. and Koutroumbas K. (2003). Pattern Recognition. Second Edition. Elsevier Academic Press, 2003.Google Scholar
  23. Tzotsos A. and Argialas D. (2006). MSEG: A Generic Region-Based Multi-Scale Image Segmentation Algorithm For Remote Sensing Imagery. Proceedings of ASPRS 2006 Annual Conference, Reno, Nevada; May 1-5, 2006.Google Scholar
  24. Tzotsos A., Iosifidis C., Argialas D. (2007). A Hybrid Texture-Based and Region-Based Multi-Scale Image Segmentation Algorithm. In: Object-Based Image Analysis – Spatial Concepts For Knowledge-Driven Remote Sensing Applications. Springer 2007.Google Scholar
  25. Vapnik V.N. (1998). Statistical Learning Theory. John-Wiley and Sons, Inc.Google Scholar
  26. Vapnik, V.N. (1995). The Nature of Statistical Learning Theory. New York, NY: Springer-Verlag.Google Scholar
  27. Yooa H.W., Park H.S., Jang D.S. (2005). Expert system for color image retrieval. Expert Systems with Applications 28 (2005) 347–357CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • A. Tzotsos
    • 1
  • D. Argialas
    • 1
  1. 1.Laboratory of Remote Sensing, Department of SurveyingSchool of Rural and Surveying Engineering, National Technical University of Athens

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