A 2D Rigid Point Registration for Satellite Imaging Using Genetic Algorithms

  • Fatiha Meskine
  • Nasreddine Taleb
  • Ahmad Almhdie-Imjabber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


Image registration is an important step for a great variety of applications such as remote sensing, medical imaging, and multi-sensor fusion-based target recognition. The objective is to find, in a huge search space of geometric transformations, an acceptable accurate solution in a reasonable time to provide better registered images for high quality products. In the broad area of global optimization methods, Genetic Algorithms form a widely accepted trade-off between global and local search strategies. They are well-investigated and have proven their applicability in many fields. In this paper, we present an efficient 2D point based rigid image registration method integrating the advantage of the robustness of GAs in finding the best transformation between two images. The algorithm is applied for registering SPOT images and the results show the effectiveness of this approach.


Image registration point registration feature points satellite images Genetic Algorithms 


  1. 1.
    Besl, P.J., McKay, N.D.: A method for registration of 3D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence 14(2), 239–256 (1992)CrossRefGoogle Scholar
  2. 2.
    Stoddart, A.J., Hilton, A.: Registration of multiple point sets. In: Proc. ICPR, pp. 40–44 (1996)Google Scholar
  3. 3.
    Goldberg, D.E.: Genetic Algorithm in search, optimization and machine learning. Addison Wesley (1989)Google Scholar
  4. 4.
    Jacq, J., Roux, C.: Registration of 3D images by genetic optimization. Pattern Recognition Letters 16, 823–841 (1995)CrossRefGoogle Scholar
  5. 5.
    Brunnström, K., Stoddart, A.J.: Genetic algorithms for free-form surface matching. In: Proc. 13th International Conference on Pattern Recognition, vol. 4, pp. 689–693 (1996)Google Scholar
  6. 6.
    Chetverikov, D., Stepanov, D., Krsek, P.: Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm. Image and Vision Computing 23(3), 299–309 (2005)CrossRefGoogle Scholar
  7. 7.
    Holland, J.H.: Adaptation in Natural and Artificial System. University of Michigan Press (1975)Google Scholar
  8. 8.
    Coley, D.: An Introduction to Genetic Algorithms for Scientists and Engineers. World Scientific Press (1999)Google Scholar
  9. 9.
    Chow, C.K., Tsui, H.T., Lee, T.: Fast Free-form Surface Registration by A New Genetic Algorithm. In: The Fifth Asian Conference on Computer Vision, Melbourne (2002)Google Scholar
  10. 10.
    da Cunha, A.L., Zhou, J., Do, M.N.: The Nansubsampled Contourlet Transform: Theory, design, and applications. IEEE Trans. on Image Processing 15(10), 3089–3101 (2006)CrossRefGoogle Scholar
  11. 11.
    Serief, C., Barkat, M., Bentoutou, Y., Benslam, M.: Robust feature points extraction for image registration based on the nonsubsampled contourlet transform. International Journal of Electronics and Communications (63), 148–152 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Fatiha Meskine
    • 1
  • Nasreddine Taleb
    • 1
  • Ahmad Almhdie-Imjabber
    • 2
    • 3
  1. 1.Laboratoire RCAMUniversity of Sidi-bel-AbbesAlgeria
  2. 2.Laboratoire PRISMEUniversité d’OrléansOrléansFrance
  3. 3.ISTO InstituteCNRSOrleansFrance

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