CFP last date
20 December 2024
Reseach Article

Performance Comparison of SAR Image Speckle Noise Removal Algorithms

by Yonatan Nagesa, S. Nagarajan, Fikiru Negesa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 18
Year of Publication: 2021
Authors: Yonatan Nagesa, S. Nagarajan, Fikiru Negesa
10.5120/ijca2021921525

Yonatan Nagesa, S. Nagarajan, Fikiru Negesa . Performance Comparison of SAR Image Speckle Noise Removal Algorithms. International Journal of Computer Applications. 183, 18 ( Jul 2021), 14-19. DOI=10.5120/ijca2021921525

@article{ 10.5120/ijca2021921525,
author = { Yonatan Nagesa, S. Nagarajan, Fikiru Negesa },
title = { Performance Comparison of SAR Image Speckle Noise Removal Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2021 },
volume = { 183 },
number = { 18 },
month = { Jul },
year = { 2021 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number18/32025-2021921525/ },
doi = { 10.5120/ijca2021921525 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:17:09.621837+05:30
%A Yonatan Nagesa
%A S. Nagarajan
%A Fikiru Negesa
%T Performance Comparison of SAR Image Speckle Noise Removal Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 18
%P 14-19
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

SAR images have achieved a prominent position in the arena of remote sensing and satellite technology. This SAR images can captured in any weather either its day or night, cloudy or sunny. SAR images will find many applications in image processing, it has many application in resource management, agriculture, mineral exploration and environmental monitoring. The useful information of the SAR image also was affected with speckle noise. The noise that corrupt the SAR (Synthetic Aperture Radar) images were affects the appearances of the image is multiplicative or granular speckle noise. Accordingly, for such speckle noise kinds different speckle noise removal method were available. The most significant method that used to remove speckle noise from SAR image is filtering technique. The SAR image speckle noise is sometimes suppressed by removing a speckle noise, using removal filter algorithm on the image before display and further analysis. To do this Median, Guided Filter (GF), Lee, Box, Adaptive or Wiener filter algorithms were used and their performances were compared in PSNR, SNR and MSE and from those all used algorithms the GF achieves better performance in high PSNR value of 37.8342.

References
  1. S. Chen, H. Wang, F. Xu, and Y. Q. Jin, ‘Target Classification Using the Deep Convolutional Networks for SAR Images’ (2016), IEEE Trans. Geosci. Remote Sens., vol. 54, no. 8, pp. 4806–4817, 2016, doi: 10.1109/TGRS.2016.2551720.
  2. Darshan kumar Jayaram, Anushree Gopalakrisnan, and S. Sharan, ‘To Improve the Azimuth Resolution in Ground Mapping Radar using Doppler Beam Sharpening Technique (2016)’, Ijrsi, vol. 3, no. 5, pp. 100–105, 2016, [Online].Available:http://www.rsisinternational.org/IJRSI/Issue27/100-105.pdf.
  3. J. Zhao, W. Guo, S. Cui, Z. Zhang, and W. Yu, ‘Convolutional Neural Network for SAR image classification at patch level’ (2016), Int. Geosci. Remote Sens. Symp., vol. 2016-Novem, no. July 2016,pp.945948,2016,doi:10.1109/IGARSS.2016.7729239.
  4. M. M. Rahman, A. Aziz, P. K. Mithun Kumar, M. A. N. U. Rajiv, and M. S. Uddin, ‘An optimized speckle noise reduction filter for ultrasound images using anisotropic diffusion technique’, Int. J. Imaging Robot., vol. 8, no. 2, pp. 55–60, 2012.
  5. K. Hara, D. Saito, and H. Shouno, ‘Analysis of function of rectified linear unit used in deep learning’, Proc. Int. Jt. Conf. Neural Networks, vol. 2015-Septe, 2015, doi: 10.1109/IJCNN.2015.7280578.
  6. G. Cybenko, ‘Approximation by superpositions of a sigmoidal function’, Math. Control. Signals, Syst., vol. 2, no. 4, pp. 303–314, 1989, doi: 10.1007/BF02551274.
  7. P. John-Baptiste, E. Zelnio, and G. E. Smith, ‘Using deep learning for SAR image optimization’, no. April 2018, p. 12, 2018, doi: 10.1117/12.2305860.
  8. J. R. R. Uijlings, K. E. A. Van De Sande, T. Gevers, and A. W. M. Smeulders, ‘Selective search for object recognition’, Int. J. Comput. Vis., vol. 104, no. 2, pp. 154–171, 2013, doi: 10.1007/s11263-013-0620-5.
  9. S. Chen et al., ‘A B7CEDGF HIB7PRQTSUDGQICWVYX HIB edCdSISIXvg5r ` CdQTw XvefCdS’, Int. Geosci. Remote Sens. Symp., vol. 2, no. 1, pp. 1–9, 2016, doi: 10.1109/IGARSS.2008.4779570.
  10. Y. Wang, P. Han, X. Lu, R. Wu, and J. Huang, ‘The performance comparison of Adaboost and SVM applied to SAR ATR’ (2006), CIE Int. Conf. RadarProc.,no.March,2006,doi:10.1109/ICR.2006.343515.
  11. C. Kang and C. He, ‘SAR image classification based on the multi-layer network and transfer learning of mid-level representations’, Int. Geosci. Remote Sens. Symp., vol. 2016-Novem, pp. 1146–1149, 2016, doi: 10.1109/IGARSS.2016.7729290.
  12. C. Belloni, A. Balleri, N. Aouf, T. J. Merlet, and J.-M. Le Caillec, ‘SAR image dataset of military ground targets with multiple poses for ATR’ (2017), no. October 2017, p. 23, 2017, doi: 10.1117/12.2277914.
  13. Raju, Nasir and Devi, Filtering Techniques to reduce Speckle Noise and Image Quality Enhancement methods on Satellite Images (2013): IOSR Journal of Computer Engineering (IOSR-JCE).
  14. V. Sarode and R. Deshmukh, Reduction of Speckle Noise and Image Enhancement of Images Using Filtering Technique (2011): International Journal of Advancements in Technology http://ijict.org/ ISSN 0976-4860. Vol 2, No 1 (January 2011) ©IJoAT.
  15. Z. Xiao-dan, L. Xuan-chi-cheng, and L. Mei, ‘The Implementation of Wiener Filtering Deconvolution Algorithm Based on the Pseudo-Random Sequence’ (2016), vol. 2, no. 1, pp. 1–5, 2016.
  16. K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, ‘Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising’ (2017), IEEE Trans. Image Process., vol. 26, no. 7, pp. 3142–3155, 2017, doi: 10.1109/TIP.2017.2662206.
  17. M. Kabkab, A. Alavi, and R. Chellappa, ‘DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size’, 2016, [Online]. Available: http://arxiv.org/abs/1606.04232.
  18. S. Caprari, and etal.Noise and speckle reduction in synthetic aperture radar imagery by nonparametric Wiener filtering.December 2000 Vol. 39, No. 35.
  19. Shastri and Rajveer K, A S TUDY OF S PECKLE N OISE R EDUCTION (2015). An International Journal (SIPIJ) Vol.6, No.3, 2015.
  20. T. Huang, George J. Yang and Gregory Y. Tang, “A Fast Two Dimensional Median Filtering Algorithm” IEEE Trans. on Acoustics, Speech and signal processing, vol. ASSP-27, no. 1 Feb. 1979.
  21. Bansal and kuar,. A Review on Speckle Noise Reduction Techniques.IOSR Journal of Computer Engineering (IOSR-JCE), Volume 16, Issue 3, Ver. I (2014), PP 74-77.
  22. Choi and Jeong, Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform.2019.11(10),1184; https://doi.org/10.3390/rs11101184
Index Terms

Computer Science
Information Sciences

Keywords

Filtration Algorithm SAR Speckle Noise Multiplicative Noise.