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Reseach Article

Article:Despeckling of Oil Spill SAR Images using Fusion Technique

by V.Radhika, Dr. G. Padmavathy
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 21 - Number 1
Year of Publication: 2011
Authors: V.Radhika, Dr. G. Padmavathy
10.5120/2474-3328

V.Radhika, Dr. G. Padmavathy . Article:Despeckling of Oil Spill SAR Images using Fusion Technique. International Journal of Computer Applications. 21, 1 ( May 2011), 26-32. DOI=10.5120/2474-3328

@article{ 10.5120/2474-3328,
author = { V.Radhika, Dr. G. Padmavathy },
title = { Article:Despeckling of Oil Spill SAR Images using Fusion Technique },
journal = { International Journal of Computer Applications },
issue_date = { May 2011 },
volume = { 21 },
number = { 1 },
month = { May },
year = { 2011 },
issn = { 0975-8887 },
pages = { 26-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume21/number1/2474-3328/ },
doi = { 10.5120/2474-3328 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:07:24.753785+05:30
%A V.Radhika
%A Dr. G. Padmavathy
%T Article:Despeckling of Oil Spill SAR Images using Fusion Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 21
%N 1
%P 26-32
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Synthetic aperture radar (SAR) oil spill images are corrupted by speckle noise due to random interference of electromagnetic waves. The speckle degrades the quality of the oil spill images and makes interpretations, analysis and classifications of SAR images harder. Therefore, some speckle mitigation is necessary prior to the processing of SAR oil spill images. In this paper, some basic speckle reduction filters like Kuan, Lee, Frost, antistrophic diffusion and SRAD filters are used. A New method is proposed for despeckling of SAR oil spill images which combines the frost filter with relaxed median filter. The proposed method gives better results when compared to other methods in terms of statistical parameters like PSNR, MSE, energy and entropy value. The approach can also reduce the computing time compared with other approaches.

References
  1. Konstantinos N. Topouzelis ,“Oil Spill Detection by SAR Images: Dark Formation Detection,Feature Extraction and Classification Algorithms” ,Sensors 2008.
  2. HANG Le Minh, DUONG Nguyen Dinh, Vietnam “ Oil Spill Detection and Classification by ALOS PALSAR at Vietnam East Sea” 7th FIG Regional Conference Spatial Data Serving People: Land Governance and the Environment – Building the Capacity Hanoi, Vietnam, 19-22 October 2009
  3. Mansor, H. Assilzadeh, H.M. Ibrahim, A. R. Mohamd. “Oil Spill Detection and Monitoring from Satellite Images ”© GISdevelopment.net..
  4. Guozhong Chen Xingzhao Liu Dept. of Electron. Eng., Shanghai Jiao Tong Univ., China “An improved wavelet-based method for SAR images denoising using data fusion technique” RADAR , IEEE conference 2006.
  5. T. Loupas, W. McDicken, and P. Allan, “An adaptive weighted median filter for speckle suppression in medical ultrasound image,” IEEE Trans. Circuits Syst., vol. 36, pp. 129–135, 1989.
  6. J. S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, pp. 165–168, 1980.
  7. V. Frost, J. Stiles, K. Shanmugan, and J. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-2, pp.157–65, 1982.
  8. D. Kuan, A. Sawchuck, T. Strand, and P. Chavel, “Adaptive noise smoothing filter for images with signal-dependent noise,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-7, no. 2, pp. 165–177, Feb
  9. A. Lopes, R. Touzi, and E. Nezry, “Adaptive speckle filters and scene heterogeneity,” IEEE Trans. Geosci. Remote Sens., vol. 28, pp. 992–1000, 1990.
  10. M. Karaman, M. A. Kutay, and G. Bozdagi, “An adaptive speckle suppression filter for medical ultrasonic imaging,” IEEE Trans. Med. Imag., vol. 14, pp. 283–292, 1995
  11. Gagnon, L. and A. Jouan, 1997, “Speckle Filtering of SAR Images—a Comparative Study between Complex-Wavelet-Based and Standard Filters,” in Proceedings of SPIE Wavelet Applications in Signal And Image Processing V, San Diego, CA, 80-91. Goodman, J. W., 1976, “Some Fundamental Properties of Speckle,” Journal of the Optical Society of America, 66(11):1145-1150. Hagg, W. and M. Sties, 1996, “The Epos Speckle Filter: A Comparison with Some Well-Known Speckle Reduction Techniques,” in Proceedings of
  12. Kuan, D. T., Sawchuk, A. A., Strand, T. C., and P. Chavel, 1987, “Adaptive Restoration of Images with Speckle,” IEEE Transactions on Acoustics, Speech, and Signal Processing, 35(3):373-383.
  13. J. S. Lee, A simple speckle smoothing filter for signal-dependent noise. IEEE Trans.On System, Man and Cybernatics, Vol. 13, No. 1, 1983, 85-89.
  14. Sheng Guofang, Hu Xin and Jiao Licheng, SAR image denoising based on data fusion. Fifth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003, 27-30 Sept. 2003, 143 - 148.
  15. Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion,” IEEE Trans. Image Process., vol. 11, pp. 1260–1270, 2002.
  16. K. Z. Abd-Elmoniem, A. B. Youssef, and Y. M. Kadah, “Real-time speckle reduction and coherence enhancement in ultrasound imaging via nonlinear anisotropic diffusion,” IEEE Trans. Biomed. Eng., vol. 49, pp. 997–1014, 2002.
  17. K. Krissian, C. F. Westin, R. Kikinis, and K. G. Vosburgh, “Oriented speckle reducing anisotropic diffusion,” IEEE Trans. Image Process., vol. 16, pp. 1412–1424, 2007.
  18. C. Sheng, Y. Xin, Y. Liping, and S. Kun, “Total variation-based speckle reduction using multi-grid algorithm for ultrasound images,” in Proc. Int. Conf. Image Analysis and Processing, 2005, vol. 3617, pp. 245–252.
  19. D. Donoho and I. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994.
  20. R. Coifman and D. Donoho, “Translation invariant de-noising,” in Lecture Notes in Statistics: Wavelets and Statistics. New York: LCNS, 1995, pp. 125–150.
  21. J. E. Odegard, H. Guo, M. Lang, C. S. Burrus, R. O. Wells, L. M. Novak, and M. Hiett, “Wavelet based SAR speckle reduction and image compression,” in Proc. SPIE
  22. Abdessamad Ben Hamza, “ Some Properties of Relaxed median filter”, IEEE DSP 97 , 1977, 957-960.
  23. Jeny Rajan, K. Kannan and M.R. Kaimal, ” An Improved Hybrid Model for Molecular Image Denoising”, Journal of Mathematical Imaging and Vision, Vol. 31 No.1 2008, 73–79.
  24. Jingfeng xiao, jing li, a. Moody A detail-preserving and flexible adaptive filter for speckle suppression in SAR imagery, International journal of Remote Sensing 2003, Vol. 24,No. 12, 2451–2465
  25. Lopez-Martinez, C. And Fabregas, X. Model based polarimetirc SAR Speckle Filter IEEE Transactions On Geoscience And Remote Sensing ,46(11):3894-3907, 2008
  26. http://cearac.poi.dvo.ru/en/db
  27. ”Oil spill detection by satellite remote sensing” Camilla Brekke,Anne H.S. Solberg.
Index Terms

Computer Science
Information Sciences

Keywords

OSSKuan Lee Frost anisotrophic diffusion SRAD PSNR MSE Entrophy Relaxed median oil spill