Skip to main content
Log in

A Review on SAR Image and its Despeckling

  • Original Paper
  • Published:
Archives of Computational Methods in Engineering Aims and scope Submit manuscript

Abstract

The method of speckle reduction is widely used in synthetic aperture radar (SAR) imagery over the last three decades. The SAR images are inherently speckled in nature. Speckle noise is a granular pattern distribution, usually modeled as a multiplicative noise that affects the SAR images, as well as all coherent images. Other SAR related problems are also discussed in this paper. Therefore, despeckling approaches are needed to improve the quality of SAR images. However, there is a trade-off between speckle reduction and the preservation of fine details in the despeckled SAR image. The reduction of the speckle noise without losing the fine details of the SAR image is a diffucult task. However, many despeckling methods have been discussed to reduce the speckle noise from the SAR images. Each method has their own norms, advantages and disadvantages. This article contains a review of some major work in the field of SAR image despeckling. Often, scientists and scholars have faced the struggle to understand the pattern distribution of the speckle noise in SAR images. Hence, a brief details about radar, SAR imaging, speckle noise in SAR images and the prevalent approaches of SAR image despeckling are reviewed here. The advantages and disadvantages of SAR image despeckling approaches are also analysed and discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data Availability

The data will be provided based on data request by the evaluation team.

References

  1. Lee JS (1980) Digital image enhancement and noise filtering by use of local statistics. IEEE Trans Pattern Anal Mach Intell PAMI-2(2):165–168

    Google Scholar 

  2. Lee J-S (1981) Speckle analysis and smoothing of synthetic aperture radar images. Comput Graph Image Process 17(1):24–32

    Google Scholar 

  3. Lee J-S (1986) Speckle suppression and analysis for synthetic aperture radar images. Opt Eng 25(5):636–643

    Google Scholar 

  4. Translation Bureau (2013) Radar definition. Public Works and Government Services Canada

  5. McGraw-Hill dictionary of scientific and technical terms. (1976) Daniel N. Lapedes, editor in chief. New York; Montreal : McGraw-Hill,[xv], 1634, A26 p

  6. Abbreviations and acronyms. Navy dot MIL. United States Navy. Retrieved 9 August 2017

  7. Small and Short-Range Radar Systems. CRC Net Base. Retrieved 9 August 2017

  8. Real Aperture Radar. Available at: http://wtlab.iis.u-tokyo.ac.jp/~wataru/lecture/rsgis/rsnote/cp4/cp4-2.htm

  9. Microwave Remote Sensing, Synthetic Aperture Radar (SAR). Available at: https://crisp.nus.edu.sg/~research/tutorial/mw.htm

  10. What is Synthetic Aperture Radar (SAR)?, Sandia National Laboratories. Available at: http://www.sandia.gov/radar/what_is_sar/

  11. Synthetic Aperture Radar. Available at: http://wtlab.iis.u-tokyo.ac.jp/~wataru/lecture/rsgis/rsnote/cp4/cp4-3.htm

  12. Cheney, Margaret (2009) Problems in synthetic-aperture radar imaging. Inverse Problems 25. Available at: http://hdl.handle.net/10945/43818

  13. Ranga Rao MS, Mahaptra PR (1997) Synthetic aperture radar: a focus on current problems. Def Sci J 47(4):517–536

    Google Scholar 

  14. Creating a SAR Image. Available at: http://www.pbs.org/wgbh/nova/spiesfly/rada_creating.html

  15. Choo AL, Chan YK, Koo VC (2012) Geometric Correction on SAR Imagery. Progress In Electromagnetics Research Symposium Proceedings, KL, Malaysia, March 27–30

  16. Toutin T (2004) Review article: geometric processing of remote sensing images: models, algorithms and methods. Int J Remote Sens 25(10):1893–1924

    Google Scholar 

  17. Cheney, Margaret. Introduction to Synthetic Aperture Radar (SAR) and SAR Interferometry. JPL. Available at: http://southport.jpl.nasa.gov/scienceapps/dixon/report2.html

  18. Sarti F. Remote sensing and SAR images processing, characterization and speckle filtering in radar images. Available: https://earth.esa.int/c/document_library/get_file?folderId=226458&name=DLFE-2125.pdf

  19. Birgir BS,Johannes RS, Benediktsson JA (2004) Combined wavelet and curvelet denoising of SAR images. In: Proceedings of IEEE 2004

  20. Bhattacharya A. Speckle Filtering/Speckle Statistics, (Slide courtesy Prof. E. Pottier and Prof. L. Ferro-Famil)

  21. Anil KJ (1989) Fundamentals of digital image processing, 1st edn. Prentice Hall, Inc, New Jersey

    MATH  Google Scholar 

  22. Chen G, Liu X (2005) Wavelet-based despeckling SAR images using neighbouring wavelet coefficients. In: Proceedings of IEEE 2005

  23. Sathit Intajag and Sakreya Chitwong. (2006) Speckle noise estimation with generalized gamma distribution. SICE-ICASE International Joint Conference 2006 Oct. 18–2 1, 2006 in Bexco, Busan, Korea

  24. Oliver C, Quegan S (1998) Understanding synthetic aperture radar images. Artech House, Boston

    Google Scholar 

  25. Bianchi T, Argenti F, Alparone L (2008) Segmentation-based map despeckling of SAR images in the undecimated wavelet domain. IEEE Trans Geosci Remote Sens 46(9):2728–2742

    Google Scholar 

  26. Dainty JC (1976) The statistics of speckle patterns. E. Wolf, Progress in Optics XIV © North-Holland

  27. Ulaby FT, Moore RK, Fung AK (1986) Microwave remote sensing, Active and Passive, Volume III. from Theory to Applications, Artech House

  28. Walessa M, Datcu M (2000) Model-based despeckling and information extraction from SAR Images. IEEE Trans Geosci Remote Sens 38(5):2258–2269

    Google Scholar 

  29. Escamilla HM, Méndez ER (1991) Speckle statistics from gamma-distributed random-phase screens. J Opt Soc Am A 8:1929–1935

    Google Scholar 

  30. Sathit Intajag and Sakreya Chitwong, (2006) Speckle Noise Estimation with Generalized Gamma Distribution. SICE-ICASE International Joint Conference, Oct 2006, 18–21, in Bexco, Busan, Korea

  31. Lee J-S (1981) Refined filtering of image noise using local statistics. Comput Graph Image Process 15(2):380–389

    Google Scholar 

  32. Frost VS et al (1982) A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans Pattern Anal Mach Intell PAMI-4:157–166

    Google Scholar 

  33. Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal dependent noise. IEEE Trans Pattern Anal Mach Intell PAMI-7(2):165–77

    Google Scholar 

  34. Lopès A, Touzi R, Nezry E (1990) Adaptive speckle filters and scene heterogeneity. IEEE Trans Geosci Remote Sens 28(6):992–1000

    Google Scholar 

  35. Lopès A, Nezry E, Touzi R, Laur H (1990) Maximum a posteriori speckle filtering and first-order texture models in SAR images. In Proc. IEEE Int Geoscience and Remote Sensing Symp. 2409–2412

  36. Lopès A, Nezry E, Touzi R, Laur H (1993) Structure detection and statistical adaptive speckle filtering in SAR images. Int J Remote Sens 14(9):1735–1758

    Google Scholar 

  37. Donoho DL, Johnstone IM (1994) Adapting to unknown smoothness via wavelet shrinkage. J Am Stat Assoc 90:1200–1224

    MathSciNet  MATH  Google Scholar 

  38. Meer P, Park R-H, Cho K (1994) Multiresolution adaptive image smoothing. Graph Models Image Process 56(2):140–148

    Google Scholar 

  39. Guo H, Odegard JE, Lang M, Gopinath RA, Selesnick IW, Burrus CS (1994) Wavelet based speckle reduction with application to SAR based ATD/R. Proc IEEE Int Conf Image Process 1:75–79

    Google Scholar 

  40. Donoho DL (1995) Denoising by soft-thresholding. IEEE Trans Inf Theory 41(3):613–627

    MATH  Google Scholar 

  41. Gagnon L, Jouan A (1997) Speckle filtering of SAR images: a comparative study between a complex-wavelet-based and standard filter. Proc SPIE 80–91

  42. Aiazzi B, Alparone L, Baronti S, Borri G (1998) Pyramid-based multiresolution adaptive filters for additive and multiplicative image noise. IEEE Trans Circuits Syst II 45(8):1092–1097

    Google Scholar 

  43. Aiazzi B, Alparone L, Baronti S (1998) Multiresolution local-statistics speckle filtering based on a ratio Laplacian pyramid. IEEE Trans Geosci Remote Sens 36(5):1466–1476

    Google Scholar 

  44. E. Hervet, R. Fjørtoft, P. Marthon, and A. Lopès (1998) Comparison of wavelet-based and statistical speckle filters. In: Proc. SPIE SAR image analysis, modelling, and techniques III, F. Posa, Ed. 3497: 43–54

  45. Sveinsson JR, Benediktsson JA (2003) Almost translation invariant wavelet transformations for speckle reduction of SAR images. IEEE Trans Geosci Remote Sens 41(510):2404–2408

    Google Scholar 

  46. Solbø S, Eltoft T (2004) Homomorphic wavelet-based statistical despeckling of SAR images. IEEE Trans Geosci Remote Sens 42(4):711–721

    Google Scholar 

  47. Dai M, Peng C, Chan AK, Loguinov D (2004) Bayesian wavelet shrinkage with edge detection for SAR image de-speckling. IEEE Trans Geo Sci Remote Sens 42(8):1642–1648

    Google Scholar 

  48. Bhuiyan MIH, Ahmad MO, Swamy MNS (2005) A new homomorphic Bayesian wavelet-based MMAE filter for despeckling SAR images. In: Proc. IEEE Int. Symp. Circuits and Systems (ISCAS) 5: 4935–4938

  49. Bhuiyan MIH, Ahmad MO, Swamy MNS (2007) Spatially adaptive wavelet-based method using the Cauchy prior for denoising the SAR images. IEEE Trans Circuits Syst Video Technol 17(4):500–507

    Google Scholar 

  50. Wu J, Yan W, Bian H, Ni W (2010) A despeckling algorithm combining curvelet and wavelet transforms of high resolution SAR images. Proc Comput Des Appl 1:302–305

    Google Scholar 

  51. Ranjani JJ, Thiruvengadam SJ (2010) Dual tree complex wavelet transform based despeckling using interscale dependency. IEEE Trans Geosci Remote Sens 48(6):2723–2731

    Google Scholar 

  52. Vijaykumar VR, Mathew A, Rao B, Santhanamari (2012) Dual tree complex wavelet transform based SAR image despeckling. In: 4th International Conference on Intelligent and Advanced Systems (ICIAS2012)

  53. Tao R, Wan H, Wang Y (2012) Artifact-free despeckling of SAR images using contourlet. IEEE Geosci Remote Sens Lett 9(5):980–984

    Google Scholar 

  54. Argenti F, Bianchi T, Lapini A, Alparone L (2012) Fast MAP despeckling based on Laplacian–Gaussian modeling of wavelet coefficients. IEEE Geosci Remote Sens Lett 9(1):13–17

    Google Scholar 

  55. Chen H, Zhang Y, Wang H, Ding C (2012) Stationary-wavelet based despeckling of SAR images using two-sided generalized gamma models. IEEE Geosci Remote Sens Lett 9(6):1061–1065

    Google Scholar 

  56. Perona P, Malik J (1990) Scale space and edge detection using anisotropic diffusion. IEEE Trans Image Process 12(8):629–639

    Google Scholar 

  57. Yu Y, Acton ST (2002) Speckle reducing anisotropic diffusion. IEEE Trans Image Process 11(11):1260–1270

    MathSciNet  Google Scholar 

  58. Yu Y, Acton ST (2004) Automated delineation of coastline from polarimetric SAR imagery. Int J Remote Sens 25(17):3423–3438

    Google Scholar 

  59. Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proc. 6th Int. Conf. Computer Vision (ICCV) pp. 839–846

  60. Zhang WG, Zhang Q, Yang CS (2011) Improved bilateral filtering for SAR image despeckling. Electron Lett 47(4):286–288

    Google Scholar 

  61. Li G-T, Wang C-L, Huang P-P, Yu W-D (2013) SAR image despeckling using a space-domain filter with alterable window. IEEE Geosci Remote Sens Lett 10(2):263267

    Google Scholar 

  62. Mastriani M, Giraldez AE (2016) Neural shrinkage for wavelet-based SAR despeckling. arXiv preprint arXiv:1608.00279

  63. Buades A, Coll B, Morel J-M (2005) A non-local algorithm for image denoising. Computer Vision and Pattern Recognition CVPR 2005. IEEE Computer Society Conference on, 20–25 June 2005

  64. Achim A, Kuruoglu EE, Zerubia J (2006) SAR image filtering based on the heavy-tailed Rayleigh model. IEEE Trans Image Process 15(9):2686–2693

    Google Scholar 

  65. Donoho DL (2006) Compressed sensing. IEEE Trans Inform Theory 52(4):1289–1306

    MathSciNet  MATH  Google Scholar 

  66. Foucher S (2008) SAR image filtering via learned dictionaries and sparse representations. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS) I: 229–232

  67. Yang M, Zhang G (2012) SAR image despeckling using overcomplete dictionary. Electron Lett 48(10):596–597

    Google Scholar 

  68. Hao Y, Feng X, Xu J (2012) Multiplicative noise removal via sparse and redundant representations over learned dictionaries and total variation. Signal Process 92(6):1536–1549

    Google Scholar 

  69. Lee J-S, Wen J-H, Ainsworth TL, Chen K-S, Chen AJ (2009) Improved sigma filter for speckle filtering of SAR imagery. IEEE Trans Geosci Remote Sens 47(1):202–213

    Google Scholar 

  70. Lee JS (1983) Digital image smoothing and the sigma filter. Comput Vis Graph Image Process 24(2):255–269

    Google Scholar 

  71. Teuber T, Lang A (2012) A new similarity measure for nonlocal filtering in the presence of multiplicative noise. Comput Stat Data Anal 56(12):3821–3842

    MathSciNet  MATH  Google Scholar 

  72. Gragnaniello D, Poggi G, Verdoliva L (2012) Classification based nonlocal SAR despeckling. In: Proc. Tyrrhenian Workshop on Advances in Radar and Remote Sensing 121–125

  73. Jojy C, Nair MS, Subrahmaniyam GRKS, Riji R (2013) Discontinuity adaptive non-local means with importance sampling unsented Kalman filter for despeckling SA images. IEEE Trans Sel Top Appl Earth Obs Remote Sens 6(4):1964

    Google Scholar 

  74. Li H-C, Hong W, Wu Y-R, Fan P-Z (2013) Bayesian wavelet shrinkage with heterogeneity-adaptive threshold for SAR image despeckling based on generalized gamma distribution. IEEE Trans Geosci Remote Sens 51(4):2388–2402. https://doi.org/10.1109/TGRS.2012.2211366

    Article  Google Scholar 

  75. Sethunadh R, Thomas T (2014) Spatially adaptive despeckling of SAR image using bivariate thresholding in directionlet domain. Electron Lett 50(1):44–45. https://doi.org/10.1049/el.2013.0971

    Article  Google Scholar 

  76. Zhu L, Zhao X, Gu M (2014) SAR image despeckling using improved detail-preserving anisotropic diffusion. Electron Lett 50(15):1092–1093. https://doi.org/10.1049/el.2014.0293

    Article  Google Scholar 

  77. Parrilli S, Poderico M (2012) Cesario Vincenzo Angelino, Luisa Verdoliva: a nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Trans Geosci Remote Sens 50(2):606–616

    Google Scholar 

  78. Kervrann C, Boulanger J, Coupé P (2007) Bayesian nonlocal means filter, image redundancy and adaptive dictionaries for noise removal. In: Proc. 1st Int. Conf. on Scale Space and Variational Methods in Computer Vision (SSVM) pp. 520–532

  79. Coupe P, Hellier P, Kervrann C, Barillot C (2008) Bayesian non local means-based speckle filtering. In: Proc. 5th IEEE Int. Symp. Biomedical Imaging: From Nano to Macro pp. 1291–1294

  80. Zhong H, Li Y, Jiao L (2011) SAR image despeckling using Bayesian non-local means filter with sigma preselection. IEEE Geosci Remote Sens Lett 8(4):809–813

    Google Scholar 

  81. de la Mata-Moya D, Diaz-Soria A, Martin-de-Nicolas J, Jarabo-Amores M-P, Pelaez VM (2014) Spatially adaptive thresholding of the empirical mode decomposition for speckle reduction purposes. In:10th European Conference on Synthetic Aperture Radar; Proceedings of Date of Conference: 3–5 June 2014

  82. Xu B, Cui Y, Li Z, Zuo B, Yang J, Song J (2015) Patch ordering-based sar image despeckling via transform-domain filtering. IEEE J Sel Top Appl Earth Obs Remote Sens 8(4):1682–1695. https://doi.org/10.1109/JSTARS.2014.2375359

    Article  Google Scholar 

  83. Zhao Y, Liu J, Zhang B, Hong W, Yirong Wu (2015) Adaptive total variation regularization based SAR image despeckling and despeckling evaluation index. IEEE Trans Geosci Remote Sens 53(5):2765–2774

    Google Scholar 

  84. Rudin LI, Osher S, Fatemi E (1992) Nonlinear total variation based noise removal algorithms. Physica D 60(1–4):259–268

    MathSciNet  MATH  Google Scholar 

  85. Shi J, Osher S (2008) A nonlinear inverse scale space method for a convex multiplicative noise model. SIAM J Imaging Sci 1(3):294–321

    MathSciNet  MATH  Google Scholar 

  86. Denis L, Tupin F, Darbon J, Sigelle M (2009) SAR image regularization with fast approximate discrete minimization. IEEE Trans Image Process 18(7):1588–1600

    MathSciNet  MATH  Google Scholar 

  87. Palsson F, Sveinsson JR, Ulfarsson MO, Benediktsson JA (2012) SAR image denoising using total variation based regularization with SURE-based optimization of the regularization parameter. In: Proc. IEEE Int. Geoscience and Remote Sensing Symp (IGARSS) pp. 2160–2163

  88. Gleich D, Kseneman M (2012) A comparison of regularization based methods for despeckling of SLC SAR images. In: Proc. 9th European Conf. Synthetic Aperture Radar (EUSAR) pp. 784–787

  89. Atto AM, Trouvé E, Nicolas J-M, Lê TT (2016) Wavelet operators and multiplicative observation models—Application to sar image time-series analysis. IEEE Trans Geosci Remote Sens 54(11):6606–6624

    Google Scholar 

  90. Gragnaniello D, Poggi G, Scarpa G, Verdoliva L (2016) SAR Image Despeckling by Soft Classification. IEEE J Sel Top Appl Earth Obs Remote Sens 9(6):2118–2130

    Google Scholar 

  91. Sivaranjani RS, Roomi MM, Senthilarasi M (2019) Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm. Appl Soft Comput 76:671–681

    Google Scholar 

  92. Xu Z, Shi Q, Chen Y, Feng W, Shao Y, Sun L, Huang X (2018) Non-stationary speckle reduction in high resolution SAR images. Digit Signal Process 73:72–82

    MathSciNet  Google Scholar 

  93. Rana VK, Suryanarayana TMV (2019) Evaluation of SAR speckle filter technique for inundation mapping. Remote Sens Appl Soc Environ 16:100271

    Google Scholar 

  94. Gokul J, Nair MS, Rajan J (2017) Guided SAR image despeckling with probabilistic non local weights. Comput Geosci 109:16–24

    Google Scholar 

  95. Sujitha AG, Vasuki P, Deepan AA (2019) Hybrid Laplacian Gaussian Based Speckle Removal in SAR Image Processing. J Med Syst 43(7):222

    Google Scholar 

  96. Liu S, Guoqing Wu, Zhang X, Zhang K, Wang P, Li Y (2017) SAR despeckling via classification-based nonlocal and local sparse representation. Neurocomputing 219:174–185

    Google Scholar 

  97. Lu Y, Gao Q, Sun D, Xia Yi, Zhang D (2016) SAR speckle reduction using Laplace mixture model and spatial mutual information in the directionlet domain. Neurocomputing 173:633–644

    Google Scholar 

  98. Farhadiani R, Homayouni S, Safari A (2019) Hybrid SAR speckle reduction using complex wavelet shrinkage and non-local PCA-based filtering. IEEE J Sel Top Appl Earth Obs Remote Sens 12(5):1489–1496

    Google Scholar 

  99. Ravani K, Saboo S, Bhatt JS (2019) A practical approach for SAR image despeckling using deep learning. In: IEEE International Geoscience and Remote Sensing Symposium pp. 2957–2960

  100. Gu F, Zhang H, Wang C, Zhang B (2017) Residual encoder-decoder network introduced for multisource SAR image despeckling. In: 2017 SAR in Big Data Era: models, methods and applications (BIGSARDATA), pp. 1–5. IEEE, 2017

  101. Denis L, Deledalle C-A, Tupin F (2019) From patches to deep learning: combining self-similarity and neural networks for SAR image despeckling. In: IGARSS 2019–2019 IEEE International Geoscience and Remote Sensing Symposium, pp. 5113–5116

  102. Vitale S, Ferraioli G, Pascazio V (2019) A new ratio image based CNN algorithm for SAR despeckling. In: IGARSS 2019–2019 IEEE international geoscience and remote sensing symposium, pp. 9494–9497

  103. Gleich D, Šipoš D (2019) Deep despeckling of SAR images. In: IGARSS 2019–2019 IEEE international geoscience and remote sensing symposium, pp. 1907–1910

  104. Cozzolino D, Verdoliva L, Scarpa G, Poggi G (2019) Nonlocal SAR image despeckling by convolutional neural networks. In: IGARSS 2019–2019 IEEE international geoscience and remote sensing symposium, pp. 5117–5120

  105. Ferraioli G, Pascazio V, Vitale S (2019) A novel cost function for despeckling using convolutional neural networks. In: 2019 Joint Urban Remote Sensing Event (JURSE), pp. 1–4

  106. Gu F, Zhang H, Wang C (2020) A two-component deep learning network for SAR image denoising. IEEE Access 8:17792–17803

    Google Scholar 

  107. Wang P, Zhang He, Patel VM (2017) SAR image despeckling using a convolutional neural network. IEEE Signal Process Lett 24(12):1763–1767

    Google Scholar 

  108. Kwak Y, Song W-J, Kim S-E (2018) Speckle-noise-invariant convolutional neural network for SAR target recognition. IEEE Geosci Remote Sens Lett 16(4):549–553

    Google Scholar 

  109. Yue D-X, Feng Xu, Jin Y-Q (2018) SAR despeckling neural network with logarithmic convolutional product model. Int J Remote Sens 39(21):7483–7505

    Google Scholar 

  110. Argenti F, Lapini A, Bianchi T, Alparone L (2013) A tutorial on speckle reduction in synthetic aperture radar images. IEEE Geosci Remote Sens Mag 1(3):6–35

    Google Scholar 

  111. Singh P, Shree R (2017) A new homomorphic and method noise thresholding based despeckling of SAR image using anisotropic diffusion. J King Saud Univ Comput Inf Sci. https://doi.org/10.1016/j.jksuci.2017.06.006

    Article  Google Scholar 

  112. Xie H, Pierce LE, Ulaby FT (2002) Statistical properties of logarithmically transformed speckle. IEEE Trans Geosci Remote Sens 40(3):721–727

    Google Scholar 

  113. Singh P, Shree R (2017) A new computationally improved homomorphic despeckling technique of SAR images. Int J Advan Res Comp Sci 8(3)

  114. Li GT, Wang CL, Huang PP, Yu WD (2013) SAR image despeckling using a space-domain filter with alterable window. IEEE Geosci Remote Sens Lett 10(2):263267

    Google Scholar 

  115. Zhang L, Zhang L, Mou X, Zhang D (2011) FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process 20(8):2378–2386

    MathSciNet  MATH  Google Scholar 

  116. Singh P, Shree R (2016) Speckle noise: modelling and implementation. Int J Control Theory Appl 9(17):8717–8727

    Google Scholar 

  117. Sattar F, Floreby L, Salomonsson G, Lovstrom B (1997) Image enhancement based on a nonlinear multiscale method. IEEE Trans Image Process 6(6):888–895

    Google Scholar 

  118. Achim A, Tsakalides P, Bezerianos A (2003) SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modelling. IEEE Trans Geosci Remote Sens 41(8):1773–1784

    Google Scholar 

  119. Singh P, Shree R (2016) Statistical modelling of log transformed speckled image. Int J Comput Sci Inf Secur 14(8):426–431

    Google Scholar 

  120. Singh P, Shree R (2017) Statistical quality analysis of wavelet based sar images in despeckling process. Asian J Electr Sci 6(2):1–18

    Google Scholar 

  121. Singh P, Shree R (2017) Quantitative dual nature analysis of mean square error in SAR image despeckling. Int J Comput Sci Eng 9:619–622

    Google Scholar 

  122. Singh P, Shree R (2016) Analysis and Effects of Speckle Noise in SAR Images. In: 2nd International conference on advances in computing, communication, and automation (ICACCA) 1–5. (IEEE International Conference)

  123. Benitz GR (1997) High-definition vector imaging. Lincoln Lab J 10(2):147–170

    Google Scholar 

  124. M Çetin, WC Karl, and DA Castañon (2000) Evaluation of a regularized SAR imaging technique based on recognitionoriented features. In: Proceeding SPIE algorithms for synthetic aperture radar imagery VII 4053: 40–51

  125. Mastriani M, Giraldez AE (2016) Enhanced directional smoothing algorithm for edge-preserving smoothing of synthetic-aperture radar images. arXiv preprint arXiv:1608.01993

  126. Synthetic Aperture Radar (SAR) Imagery, Sandia National Laboratories, Airborne ISR. Available at: http://www.sandia.gov/RADAR/imagery/

  127. Moreira A, Prats-Iraola P, Younis M, Krieger G, Hajnsek I, Papathanassiou KP (2013) A tutorial on synthetic aperture radar. IEEE Geosci Remote Sens Mag. https://doi.org/10.1109/MGRS.2013.2248301

    Article  Google Scholar 

Download references

Funding

This Research Received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

The authors reviewed some of the major work in the field of SAR image despeckling.

Corresponding author

Correspondence to Achyut Shankar.

Ethics declarations

Conflict of interest

The authors of this research article declares that no conflict of interest in preparing this research article.

Consent for Publication

All the authors of this paper have shown their Participation voluntarily.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Singh, P., Diwakar, M., Shankar, A. et al. A Review on SAR Image and its Despeckling. Arch Computat Methods Eng 28, 4633–4653 (2021). https://doi.org/10.1007/s11831-021-09548-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11831-021-09548-z

Navigation