Super Pixel Segmentation with Nakagami Model for SAR Images

Authors

  • M. Rangaswamy  Electronics and communication, Brindavan institute of technology and science, Kurnool, Andhra Pradesh, India
  • G. Ragasudha  Electronics and communication, Brindavan institute of technology and science, Kurnool, Andhra Pradesh, India
  • P. surekha  Electronics and communication, Brindavan institute of technology and science, Kurnool, Andhra Pradesh, India
  • G. Ghousiyabhanu  Electronics and communication, Brindavan institute of technology and science, Kurnool, Andhra Pradesh, India
  • G. Manasa  Electronics and communication, Brindavan institute of technology and science, Kurnool, Andhra Pradesh, India

Keywords:

Limited blend models (FMMs), manufactured gap radar (SAR) picture, super pixel division.

Abstract

We propose a blend based super pixel division strategy for engineered opening radar or synthetic aperture radar (SAR) pictures. The strategy utilizes SAR picture amplitudes and pixel arranges as highlights. The element vectors are demonstrated measurably by taking into account the SAR picture measurements. We turn to limited blend models to group the pixels into super pixels. After super pixel division, we arrange diverse land covers, for example, urban, arrive, also, lake utilizing the highlights removed from each super pixel. Based on the arrangement comes about got on genuine Terra SAR-X pictures, it is demonstrated that the outcomes acquired by the proposed super pixel technique are fit for accomplishing a more precise characterization contrasted and those acquired by cutting edge super pixel division techniques, for example, fast move, turbo pixels, basic straight iterative grouping, and pixel force and area closeness

References

  1. A. Vedaldi and S. Soatto, "Quick shift and kernel methods for mode seeking," in European Conference Computer Vision, ser. Lecture Notes in Computer Science, vol. 5305. Berlin, Germany: Springer-Verlag, 2008, pp. 705–718.
  2. R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Susstrunk, "SLIC superpixels compared to state-of-the-art superpixel methods," IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 11, pp. 2274–2281, Nov. 2012.
  3. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 8, pp. 888–905, Aug. 2000.
  4. P. Felzenszwalb and D. Huttenlocher, "Efficient graph-based image segmentation," Int. J. Comput. Vis., vol. 59, no. 2, pp. 167–181, 2004.
  5. A. Levinshtein, A. Stere, K. Kutulakos, D. J. Fleet, S. J. Dickinson, and K. Siddiqi, "TurboPixels: Fast superpixels using geometric flows," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 12, pp. 2290–2297, Dec. 2009.
  6. C. Oliver and S. Quegan, Understanding Synthetic Aperture Radar Images. Norwood, MA, USA: Artech House, 1998.
  7. B. Liu, H. Hu, H. Wang, K. Wang, X. Liu, and W. Yu, "Superpixel-based classification with an adaptive number of classes for polarimetric SAR images," IEEE Geosci. Remote Sens. Lett., vol. 51, no. 2, pp. 907–924, Feb. 2013.
  8. F. Qin, J. Guo, and F. Lang, "Superpixel segmentation for polarimetric SAR imagery using local iterative clustering," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 13–17, Jan. 2015.
  9. D. Xiang, T. Tang, L. Zhao, and Y. Su, "Superpixel generating algorithm based on pixel intensity and location similarity for SAR image classifi- cation," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 6, pp. 1414–1418, Nov. 2013. 10H. Feng, B. Hou, and M. Gong, "SAR image despeckling based on local homogeneous-region segmentation by using pixel-relativity measurement," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 7, pp. 2724–2737, Jul. 2011. 11M. Wertheimer, "Laws of organization in perceptual forms (partial translation)," in A Sourcebook of Gestalt Psychology. San Diego, CA, USA: Harcourt, Brace and Company, 1938, pp. 71–88.
  10. J. M. Nicolas and F. Tupin, "Gamma mixture modeled with "second kind statistics": Application to SAR image processing," in Proc. IGARSS, 2002, pp. 2489–2491.
  11. G. Moser, J. Zerubia, and S. Serpico, "Dictionary-based stochastic expectation–maximization for SAR amplitude probability density function estimation," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 1, pp. 188–199, Jan. 2006.
  12. K. Kayabol and J. Zerubia, "Unsupervised amplitude and texture classifi- cation of SAR images with multinomial latent model," IEEE Trans. Image Process., vol. 22, no. 2, pp. 561–572, Feb. 2013.
  13. K. Kayabol, "Histogram-based contextual classification of SAR images," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 33–37, Jan. 2015

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2018-04-30

Issue

Volume 4, Issue 4, March-April-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
M. Rangaswamy, G. Ragasudha, P. surekha, G. Ghousiyabhanu, G. Manasa, " Super Pixel Segmentation with Nakagami Model for SAR Images, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 4, pp.885-890, March-April-2018.