Cluster Computing

, Volume 21, Issue 1, pp 229–237 | Cite as

SAR images denoising using a novel stochastic diffusion wavelet scheme

  • A. RaviEmail author
  • M. N. Giriprasad
  • P. V. Naganjaneyulu


The rapid processing of remote sensing (RS) images is crucial in real-time monitoring. However, the computation cost of RS is high and traditional methods are not effective. Cloud computing with its capability of parallel computing provides an effective service for executing RS processing. A cloud-integrated web platform for maintaining geographic information system (GIS) and RS application such as oil spill detection, meteorological monitoring through synthetic aperture radar (SAR) images is a fast-growing application. SAR RS helps also in studying land and sea-based phenomena, especially the capability of acquiring weather forecasts all-day. Wavelet transform is a very well-known tool for prime applications in time series, function estimation, and image analysis. In this work, an effective non-deterministic polynomial computation technique for noise mitigation of SAR Images which has its basis on Hybrid wavelet transform (WT) is proposed. Proper noise reduction parameters should be chosen while selecting wavelet transform for SAR images. So, in the proposed method Stochastic diffusion search (SDS) optimization algorithm is utilized for selecting the optimal noise reduction parameter thus leading better filtration performance. Effective choice of wavelet noise mitigation techniques includes wavelet function, decomposition levels, and threshold selection rules for improved noise reduction. Experimental results show that MSE, PSNR and standard deviation are enhanced with the proposed method.


Synthetic aperture radar (SAR) Wavelet analysis Hybrid wavelet transform Stochastic diffusion search (SDS) Optimization algorithm 


  1. 1.
    Wang, P., Wang, J., Chen, Y., Ni, G.: Rapid processing of remote sensing images based on cloud computing. Future Gener. Comput. Syst. 29(8), 1963–1968 (2013)CrossRefGoogle Scholar
  2. 2.
    Li, Y., Gong, H., Feng, D., Zhang, Y.: An adaptive method of speckle reduction and feature enhancement for SAR images based on curvelet transform and particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 49(8), 3105–3116 (2011)CrossRefGoogle Scholar
  3. 3.
    Horgan, G.: Wavelets for SAR image smoothing. Am. Soc. Photogramm. Remote Sens. 64(12), 1171–1177 (1998)Google Scholar
  4. 4.
    Ali, S.M., Javed, M.Y., Khattak, N.S.: Wavelet-based de-speckling of synthetic aperture radar images using adaptive and mean filters. Proc. World Acad. Sci. Eng. Technol. 25, 39–43 (2007). Venice (Italy)Google Scholar
  5. 5.
    Lee, J.S., Wen, J.H., Ainsworth, T.L., Chen, K.S., Chen, A.J.: Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans. Geosci. Remote Sens. 47(1), 202–213 (2009)CrossRefGoogle Scholar
  6. 6.
    Omran, M.G., Salman, A.: Probabilistic stochastic diffusion search. International Conference on Swarm Intelligence, pp. 300–307. Springer, Berlin (2012)CrossRefGoogle Scholar
  7. 7.
    Williams, H., Bishop, M.: Stochastic diffusion search: a comparison of swarm intelligence parameter estimation algorithms with RANSAC. Algorithms 7(2), 206–228 (2014)Google Scholar
  8. 8.
    Wu, Y., Yuan, X.: Wavelet speckle reduction for SAR imagery based on edge detection. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVII. Part B1, pp. 117–122. Beijing (2008)Google Scholar
  9. 9.
    Chen, W.F., Liu, A.L., Xia, J.J., Duan, C.L., Yang, S.S., Hu, W.: Study on synthetic aperture radar image denoising algorithm. Applied Mechanics and Materials, vol. 599, pp. 1734–1737. Trans Tech Publications, Stafa-Zurich (2014)Google Scholar
  10. 10.
    Wang, L., Ma, Y., Yan, J., Chang, V., Zomaya, A.Y.: pipsCloud: High performance cloud computing for remote sensing big data management and processing. Future Gener. Comput. Syst. (2016). doi: 10.1016/j.future.2016.06.009
  11. 11.
    Hui-shu, H., Guo-jun, Z.: Remote system for oil spill detection based on ZigBee and GIS. Int J Online Eng. 12(11), 4–9 (2016)CrossRefGoogle Scholar
  12. 12.
    Zhong, W., Zhuang, Y., Sun, J., Gu, J.: The cloud computing load forecasting algorithm based on wavelet support vector machine. In: Proceedings of the Australasian Computer Science Week Multiconference, ACM, p. 38. (2017)Google Scholar
  13. 13.
    Hanbay, K., Talu, M.F.: Segmentation of SAR images using improved artificial bee colony algorithm and neutrosophic set. Appl. Soft Comput. 21, 433–443 (2014)CrossRefGoogle Scholar
  14. 14.
    Gyaourova, A., Kamath, C., Fodor, I.K.: Undecimated wavelet transforms for image de-noising, p. 18. Lawrence Livermore National Lab., Livermore, CA, Report (2002)Google Scholar
  15. 15.
    Fazel, M. A., Homayouni, S., Akbari, V., Pari, M. M.: Speckle reduction of SAR images using curvelet and wavelet transforms based on spatial features characteristics. In 2012 IEEE International Geoscience and Remote Sensing Symposium (pp. 2148–2151). IEEE (2012)Google Scholar
  16. 16.
    Zakeri, F., Zoej, M.J.V.: Adaptive method of speckle reduction based on curvelet transform and thresholding neural network in synthetic aperture radar images. J. Appl. Remote Sens. 9(1), 095043–095043 (2015)CrossRefGoogle Scholar
  17. 17.
    Amin, M.G., Ahmad, F.: Wideband synthetic aperture beamforming for through-the-wall imaging. IEEE Signal Process. Mag. 25(4), 110–113 (2008). [lecture notes]CrossRefGoogle Scholar
  18. 18.
    al-Rifaie, M.M., Bishop, J.M.: Stochastic diffusion search review. Paladyn J Behav. Robot. 4(3), 155–173 (2013)Google Scholar
  19. 19.
    al-Rifaie, M. M., Bishop, M. J., Blackwell, T.: An investigation into the merger of stochastic diffusion search and particle swarm optimisation. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 37–44 . ACM (2011)Google Scholar
  20. 20.
    Niu, Y., Shen, L.: Wavelet denoising using the Pareto optimal threshold. IJCSNS 7(1), 30 (2007)Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • A. Ravi
    • 1
    Email author
  • M. N. Giriprasad
    • 2
  • P. V. Naganjaneyulu
    • 3
  1. 1.JNTUHHyderabadIndia
  2. 2.Department of ECEJNTUAAnantapurIndia
  3. 3.Department of ECEMVR College of Engineering & TechnologyParitalaIndia

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