An Approach to Automatic Target Recognition in Radar Images Using SVM

  • Noslen Hernández
  • José Luis Gil Rodríguez
  • Jorge A. Martin
  • Francisco Silva Mata
  • Ricardo González
  • Raúl Álvarez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4225)


This paper introduces an Automatic Target Recognition (ATR) method based on X Band Radar image processing. A software which implements this method was developed following four principal stages: digital image formation, image preprocessing, feature selection through a combination of C4.5 Decision Tree and PCA and classification using SVM. The automatic process was validated using two images sets, one of them containing real images with natural noise levels and the other with different degrees of impulsive noise contamination. The method achieves a very nice computation behavior and effectiveness, high accuracy and robustness in noise environments with a low storage memory and high decision speed.


target recognition X Band radar SVM image processing feature selection 


  1. 1.
    Gallego, J., Pérez, F.: Detección de Blancos Extensos en Entornos Marinos y Terrestres Basada en Descriptores (May 14, 2006) Available in Internet at:
  2. 2.
    Turhan-Sayan, G.: Real Time Electromagnetic Target Classification Using a Novel Feature Extraction Technique with PCA-Based Fusion. IEEE Trans. on Antennas and Propagation 53(2) (February 2005)Google Scholar
  3. 3.
    Bairachefski, A.M.: Sistemas de radiolocalización naval, Editora Transporte, Moscú (1982)Google Scholar
  4. 4.
    Shinozawa,, et al.: A weather radar image prediction method in local parallel computation. In: International Conference on Neural Networks (1994)Google Scholar
  5. 5.
    Skolnik, M.I.: Introduction to Radar Systems, 2. Ausgabe, p. 1563. McGraw-Hill, New York (1980)Google Scholar
  6. 6.
    Martin, J.A. y J.L. Gil: Cálculo de la navegación de altura empleando señales de posicionamiento global e imágenes meteorológicas. IPEN Journal, No. 29, Published by Pan American Institute of Naval Engineering, ISSN – 1011 – 5951 (2005)Google Scholar
  7. 7.
    González, R.C., Woods, R.E.: Digital Image Proccesing, 2nd edn. Addison- Wesley Publishing Company (2002)Google Scholar
  8. 8.
    Gil, J.L.: Estado actual de la representación y análisis de textura en imágenes (inédito) (2005)Google Scholar
  9. 9.
    Silva, F.J.: Estado actual de la representación y análisis de formas (inédito) (2005)Google Scholar
  10. 10.
    Quinlan, R.J.: C4.5: Programs for Machine Learnig (Morgan Kaufmann Series in Machine Learning). Paperback- January 15 (1993)Google Scholar
  11. 11.
    Jackson, J.E.: A User’s Guide to Principal Components. John Wiley & Sons, Chichester (1991)zbMATHCrossRefGoogle Scholar
  12. 12.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)zbMATHGoogle Scholar
  13. 13.
    Scholkopf, C., Burges, J., Smola, A.: Advances in Kernel methods: Support Vector Learning. MIT Press, Cambridge (1999)Google Scholar
  14. 14.
    Cristianini, Shawe-Taylor, J.: An introduction to Support Vector Machine. Cambridge University Press, Cambridge (2000)Google Scholar
  15. 15.
    Burges, C.J.C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1999)CrossRefGoogle Scholar
  16. 16.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines, Dpt. Computer Science and Information Engineering, National Taiwan University (2003) (May 14, 2006), available in Internet at
  17. 17.
    Computer Science 831: Knowledge Discovery in Databases. Dept. of Computer Science. University of Regina. Canada (May 15, 2006), available in Internet at

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Noslen Hernández
    • 1
  • José Luis Gil Rodríguez
    • 1
  • Jorge A. Martin
    • 2
  • Francisco Silva Mata
    • 1
  • Ricardo González
    • 1
  • Raúl Álvarez
    • 2
  1. 1.Advanced Technologies Application CenterMIMBASCiudad de la Habana, PlayaCuba
  2. 2.“Granma” Navy Academy, Micro X, AlamarCiudad de La Habana

Personalised recommendations