Discrete Dynamics in Nature and Society
Volume 2009 (2009), Article ID 601638, 11 pages
doi:10.1155/2009/601638
Research Article

Pyramidal Watershed Segmentation Algorithm for High-Resolution Remote Sensing Images Using Discrete Wavelet Transforms

K. Parvathi,1 B. S. Prakasa Rao,2 M. Mariya Das,3 and T. V. Rao4

1Department of Electronics and Communication Engineering (ECE), Jagannath Institute for Technology and Management (JITM), Parlakhemundi, Gajapati 761211, Orissa, India
2Gandi Institute of Technology and Management, Pinagadi, Visakhapatnam 531173, Andhra Pradesh, India
3Department of Instrument Technology, Andhra University, Visakhapatnam 530003, Andhra Pradesh, India
4Department of Geo-Engineering, Andhra University, Visakhapatnam 530003, Andhra Pradesh, India

Received 10 December 2008; Revised 12 May 2009; Accepted 29 June 2009

Academic Editor: B. Sagar

Copyright © 2009 K. Parvathi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. L. M. Lifshitz, Image segmentation using global knowledge and a priori information, Ph.D. thesis, University of North Carolina, Chapel Hill, NC, USA, 1987.
  2. T. Lindeberg, Discrete scale space theory and the scale space primal sketch, Ph.D. thesis, Royal Institute of Technology, Stockholm, Sweden, May 1991.
  3. F. Meyer and S. Beucher, “Morphological segmentation,” Journal of Visual Communication and Image Representation, vol. 1, no. 1, pp. 21–46, 1990.
  4. L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583–598, 1991. View at Publisher · View at Google Scholar
  5. A. Bieniek and A. Moga, “An efficient watershed algorithm based on connected components,” Pattern Recognition, vol. 33, no. 6, pp. 907–916, 2000. View at Publisher · View at Google Scholar
  6. F. Meyer, “Leveling and morphological segmentation,” in Proceedings of International Symposium on Computer Graphics, Image Processing, and Vision (SIBAGRAPI '98), pp. 28–35, Rio de Janeiro, Brazil, October 1998.
  7. C. R. Jung and J. Scharcanski, “Robust Watershed segmentation using the wavelet transforms,” in Proceedings of the XV Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI '02), pp. 1530–1834, February 2000.
  8. C. R. Jung, “Multiscale image segmentation using wavelets and watersheds,” in Proceedings of the XVI Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI '02), pp. 1530–1834, March 2003.
  9. K. Haris, S. N. Efstratiadis, N. Maglaveras, and A. K. Katsaggelos, “Hybrid image segmentation using watersheds and fast region merging,” IEEE Transactions on Image Processing, vol. 7, no. 12, pp. 1684–1699, 1998.
  10. P. Jackway, “Morphological multiscale gradient watershed image analysis,” in Proceedings of the 9th Scandivania Conference on Image Analysis, pp. 87–94, 1995.
  11. J. M. Gauch, “Image segmentation and analysis via multiscale gradient watershed hierarchies,” IEEE Transactions on Image Processing, vol. 8, no. 1, pp. 69–79, 1999.
  12. J. Weickert, “Efficient image segmentation using partial differential equations and morphology,” Pattern Recognition, vol. 34, no. 9, pp. 1813–1824, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  13. J. B. Kim and H. J. Kim, “A wavelet-based watershed image segmentation for VOP generation,” in Proceedings of International Conference on Pattern Recognition, vol. 16, no. 3, pp. 505–508, Queboc city, Canada, August 2002.
  14. J.-B. Kim and H.-J. Kim, “Multiresolution-based watersheds for efficient image segmentation,” Pattern Recognition Letters, vol. 24, no. 1–3, pp. 473–488, 2003. View at Publisher · View at Google Scholar
  15. H. T. Nguyen, M. Worring, and R. Van den Boomgaard, “Watersnakes: energy-driven watershed segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 3, pp. 330–342, 2003. View at Publisher · View at Google Scholar
  16. H. Liu, Z. Chen, X. Chen, and Y. Chen, “Multiresolution medical image segmentation based on wavelet transform,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (EMBS '05), vol. 7, pp. 3418–3421, Shanghai, China, September 2005.
  17. S. G. Mallat, “Theory for multiresolution signal decomposition: the wavelet representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674–693, 1989. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  18. P. Soille, Morphological Image Analysis Principles & Applications, Springer, Berlin, Germany, 1999. View at MathSciNet
  19. L. Vincent and P. Soille, “Watersheds in digital spaces: an efficient algorithm based on immersion simulations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 13, no. 6, pp. 583–598, 1991. View at Publisher · View at Google Scholar