Reduction of Spatially Correlated Speckle in Textured SAR Images

Authors

  • Oleksii Rubel
  • Vladimir Lukin
  • Sergiy Krivenko
  • Vladimir Pavlikov
  • Simeon Zhyla
  • Eduard Tserne

DOI:

https://doi.org/10.47839/ijc.20.3.2276

Keywords:

Synthetic aperture radar image, Denoising, Despeckling efficiency, Visual quality

Abstract

Synthetic aperture radars (SARs) provide a lot of images that can be used for numerous applications. A problem with acquired images is that they are corrupted by speckle which is a noise-like phenomenon with multiplicative nature. In addition, speckle is non-Gaussian and it is often spatially correlated. A typical task in SAR image processing is despeckling and many methods have been already proposed. However, most of them do not take noise spatial correlation into account during denoising. In this paper, we show how this can be done in despeckling based on discrete cosine transform. The use of frequency-dependent thresholds leads to sufficient improvement of denoising efficiency in terms of visual quality metrics. Moreover, we consider quite complex structure texture images for which noise removal is usually problematic and can lead to information loss. Comparison to the well-known local statistic Lee and Frost filters, extended DCT-based filter is carried out for different remote sensing systems including Sentinel-1 and Sentinel-2.

References

J.-S.Lee, E. Pottier, Polarimetric Radar Imaging: From Basics to Applications, CRC Press, 2009, 422 p.

N. Kussul, S. Skakun, A. Shelestov, O. Kussul, “The use of satellite SAR imagery to crop classification in Ukraine within JECAM project,” Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Quebec City, Canada, July 2014, pp. 1497-1500. https://doi.org/10.1109/IGARSS.2014.6946721.

A. Roth, U. Marschalk, K. Winkler, B. Schättler, M. Huber, I. Georg, C. Künzer, S. Dech, “Ten years of experience with scientific TerraSAR-X data utilization,” Remote Sensing, vol. 10, issue 8, pp. 1170, 2018. https://doi.org/10.3390/rs10081170.

C. Oliver, S. Quegan, Understanding Synthetic Aperture Radar Images, SciTech Publishing, 2004, 486 p.

A. G. Mullissa, C. Persello, V. Tolpekin, “Fully convolutional networks for multi-temporal SAR image classification,” Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Valencia, Spain, July 2018, pp. 6635-6638. https://doi.org/10.1109/IGARSS.2018.8518780.

Y. Makinen, L. Azzari, A. Foi, “Exact transform-domain noisevariance for collaborative filtering of stationary correlated noise,” Proceedings on IEEE International Conference on Image Processing (ICIP), 22-25 September 2019, pp. 185-189. https://doi.org/10.1109/ICIP.2019.8802964.

J.S. Lee, “Digital image enhancement and noise filtering by use of local statistics,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-2, issue 2, pp. 165-168, 1980.

https://doi.org/10.1109/TPAMI.1980.4766994.

V.S. Frost, J.A. Stiles, K.S. Shanmugan, J.C. Holtzman, “A model for radar images and its application to adaptive digital filtering of multiplicative noise,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-4, issue 2, pp. 157-166, 1982. https://doi.org/10.1109/TPAMI.1982.4767223.

R. A. Touzi, “Review of speckle filtering in the context of estimation theory,” IEEE Transactions on Geoscience and Remote Sensing, vol. 40, issue 11, pp. 2392-2404, 2002. https://doi.org/10.1109/TGRS.2002.803727.

P. Kupidura, “Comparison of filters dedicated to speckle suppression in SAR images,” ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Prague, Czech Republic, July 12-19, 2016, pp. 269-276. https://doi.org/10.5194/isprsarchives-XLI-B7-269-2016.

C.A. Deledalle, L. Denis, S. Tabti, F. Tupin, “MuLoG, or how to apply Gaussian denoisers to multi-channel SAR speckle reduction?,” IEEE Transactions on Image Processing, vol. 26, issue 9, pp. 4389-4403, 2017. https://doi.org/10.1109/TIP.2017.2713946.

J.-S. Lee, J.-H. Wen, T.L. Ainsworth, K-S.Chen, A.J. Chen, “Improved sigma filter for speckle filtering of SAR imagery,” IEEE Transactions on Geoscience and Remote Sensing, vol. 47, issue 1, pp. 202-213, 2009. https://doi.org/10.1109/TGRS.2008.2002881.

S. Abramov, O. Rubel, V. Lukin, A. Shelestov, M. Lavreniuk, “Speckle reducing for Sentinel-1 SAR data,” Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA, December 4, 2017, pp. 2353-2356. https://doi.org/10.1109/IGARSS.2017.8127463.

S. Solbo, T. Eltoft, “A stationary wavelet domain Wiener filter for correlated speckle,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, issue 4, pp. 1219-1230, 2008. https://doi.org/10.1109/TGRS.2007.912718.

M. Matrecano, G. Poggi, L. Verdoliva, “Improved BM3D for correlated noise removal,” Proceedings of International Conference on Computer Vision, Theory and Applications, Springer, Rome, 2012, pp. 129-134.

S. Parrilli, M. Poderico, C.V. Angelino, L. Verdoliva, “A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, issue 2, pp. 606-616, 2012. https://doi.org/10.1109/TGRS.2011.2161586.

D. Cozzolino, L. Verdoliva, G. Scarpa, G. Poggi, “Nonlocal CNN SAR image despeckling,” Remote Sensing, vol. 12, issue 6, p.1006, 2020. https://doi.org/10.3390/rs12061006.

P. Chatterjee, P. Milanfar, “Is denoising dead?,” IEEE Transactions on Image Processing, vol. 19, issue 4, pp. 895-911, 2010. https://doi.org/10.1109/TIP.2009.2037087.

P. Chatterjee, P. Milanfar, “Practical bounds on image denoising: From estimation to information,” IEEE Transactions on Image Processing, vol. 20, issue 5, pp. 1221-1233, 2011. https://doi.org/10.1109/TIP.2010.2092440.

P. Milanfar, “A tour of modern image filtering,” IEEE Signal Processing Magazine, vol. 30, pp. 106-128, 2013. https://doi.org/10.1109/MSP.2011.2179329.

B. Aiazzi, L. Alparone, S. Baronti, R. Carla, “Adaptive texture-preserving filtering of multitemporal ERS-1 SAR images,” Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Singapore, Singapore, August 3-8, 1997, pp. 2066–2068.

X. Liu, S. Bourennane, C. Fossati, “Nonwhite noise reduction in hyperspectral images,” IEEE Geoscience and Remote Sensing Letters, vol. 9, pp. 368-372, 2012. https://doi.org/10.1109/LGRS.2011.2169041.

O. Rubel, V. Lukin, K. Egiazarian, “Additive spatially correlated noise suppression by robust block matching and adaptive 3D filtering,” Journal of Imaging Science and Technology, vol. 62, issue 6, pp. 60401-1-60401-11, 2018. https://doi.org/10.2352/J.ImagingSci.Technol.2018.62.6.060401.

B. Goossens, A. Pizurica, W. Philips, “Removal of correlated noise by modeling the signal of interest in the wavelet domain,” IEEE Transactions on Image Processing, vol. 18, issue 6, pp. 1153-1165, 2009. https://doi.org/10.1109/TIP.2009.2017169.

M. Lebrun, M. Colom, J. M. Morel, “The noise clinic: A universal blind denoising algorithm,” Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, France, October 27-30, 2014, pp. 2674-2678. https://doi.org/10.1109/ICIP.2014.7025541.

O. Rubel, V. Lukin, A. Rubel, K. Egiazarian, “NN-based prediction of Sentinel-1 SAR image filtering efficiency,” Geosciences, vol. 9, no. 7, p. 290, 2019. https://doi.org/10.3390/geosciences9070290.

L. Zhang, X. Mou, D. Zhang, “FSIM: a feature similarity index for image quality assessment,” IEEE Transactions on Image Processing, vol. 20, pp. 2378-2386, 2011. https://doi.org/10.1109/TIP.2011.2109730.

E. Larson, D. Chandler, “Most apparent distortion: Full-reference image quality assessment and the role of strategy,” Journal of Electronic Imaging, vol. 19, issue 1, pp. 011006:1–011006:21, 2010. https://doi.org/10.1117/1.3267105.

W. Xue, L. Zhang, X. Mou, A. Bovik, “Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,” IEEE Transactions in Image Processing, vol. 23, pp. 684-695, 2014. https://doi.org/10.1109/TIP.2013.2293423.

Z. Wang, E. Simoncelli, A. Bovik, “Multiscale structural similarity for image quality assessment,” Proceedings of the Thirty-Seventh Asilomar Conference on Signals, Systems & Computers, Pacific Grove, CA, USA, 9-12 November 2003, pp. 1398-1402.

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Published

2021-09-30

How to Cite

Rubel, O., Lukin, V., Krivenko, S., Pavlikov, V., Zhyla, S., & Tserne, E. (2021). Reduction of Spatially Correlated Speckle in Textured SAR Images. International Journal of Computing, 20(3), 319-327. https://doi.org/10.47839/ijc.20.3.2276

Issue

2021, Volume 20, Issue 3

Section

Articles

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