volume | PIER Journals

Abstract

High resolution Synthetic Aperture Radar (SAR) images are affected by speckle noise, which considerably reduces their visibility and complicates the target identification. In this paper, a new Compressive Sensing (CS) method is proposed to reduce the speckle noise effects of complex valued SAR images. The sparsity of the SAR images allows solving the CS problem using Multiple Measurements Vector (MMV) configuration. Therefore, a special weighted norm is constructed to solve the optimization problem, so that the Variance-Based Joint Sparsity (VBJS) model is used to calculate the weights. An efficient Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem. The obtained results using raw complex-valued measurements with ground truth demonstrate the effectiveness of the proposed despeckling method in terms of both image quality and computational cost.

1. Curlander, J. C. and R. N. McDonough, Synthetic Aperture Radar, Vol. 11, Wiley, 1991.

2. Moreira, A., P. Prats-Iraola, M. Younis, G. Krieger, I. Hajnsek, and K. P. Papathanassiou, "A tutorial on synthetic aperture radar," IEEE Geoscience and Remote Sensing Magazine, Vol. 1, No. 1, 6-43, 2013.
doi:10.1109/MGRS.2013.2248301 Google Scholar

3. Massonnet, D. and J. C. Souyris, Imaging with Synthetic Aperture Radar, EPFL Press, 2008.
doi:10.1201/9781439808139

4. Lee, J. S., "Digital image enhancement and noise filtering by use of local statistics," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 2, No. 2, 165-168, 1980.
doi:10.1109/TPAMI.1980.4766994 Google Scholar

5. Kuan, D. T., A. A. Sawchuk, T. C. Strand, and P. Chavel, "Adaptive noise smoothing filter for images with signal-dependent noise," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, No. 2, 165-177, 1985.
doi:10.1109/TPAMI.1985.4767641 Google Scholar

6. Frost, V. S., J. A. Stiles, K. S. Shanmugan, and 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. 4, No. 2, 157-166, 1982.
doi:10.1109/TPAMI.1982.4767223 Google Scholar

7. Lopes, A., R. Touzi, and E. Nezry, "Adaptive speckle filters and scene heterogeneity," IEEE Transactions on Geoscience and Remote Sensing, Vol. 28, No. 6, 992-1000, 1990.
doi:10.1109/36.62623 Google Scholar

8. Lopes, A., E. Nezry, R. Touzi, and H. Laur, "Structure detection and statistical adaptive speckle filtering in SAR images," International Journal of Remote Sensing, Vol. 14, No. 9, 1735-1758, 1993.
doi:10.1080/01431169308953999 Google Scholar

9. Bioucas-Dias, J. M. and M. A. Figueiredo, "Multiplicative noise removal using variable splitting and constrained optimization," IEEE Transactions on Image Processing, Vol. 19, No. 7, 1720-1730, 2010.
doi:10.1109/TIP.2010.2045029 Google Scholar

10. Xu, B., Y. Cui, Z. Li, B. Zuo, J. Yang, and J. Song, "Patch ordering-based SAR image despeckling via transform-domain filtering," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, No. 4, 1682-1695, 2014.
doi:10.1109/JSTARS.2014.2375359 Google Scholar

11. Sabanci, K., E. Yigit, A. Toktas, and A. Kayabasi, "A Hue-domain filtering technique for enhancing spatial sampled compressed sensing-based SAR images," IET Radar, Sonar & Navigation, Vol. 13, No. 3, 357-367, 2019.
doi:10.1049/iet-rsn.2018.5210 Google Scholar

12. Ozcan, C., B. Sen, and F. Nar, "Sparsity-driven despeckling for SAR images," IEEE Geoscience and Remote Sensing Letters, Vol. 13, No. 1, 115-119, 2015.
doi:10.1109/LGRS.2015.2499445 Google Scholar

13. Feng, W., G. Nico, and M. Sato, "GB-SAR interferometry based on dimension-reduced compressive sensing and multiple measurement vectors model," IEEE Geoscience and Remote Sensing Letters, Vol. 16, No. 1, 70-74, 2018.
doi:10.1109/LGRS.2018.2866600 Google Scholar

14. Borcea, L. and I. Kocyigit, "A multiple measurement vector approach to synthetic aperture radar imaging," SIAM Journal on Imaging Sciences, Vol. 11, No. 1, 770-801, 2018.
doi:10.1137/17M1142065 Google Scholar

15. Potter, L. C., E. Ertin, J. T. Parker, and M. Cetin, "Sparsity and compressed sensing in radar imaging," Proceedings of the IEEE, Vol. 98, No. 6, 1006-1020, 2010.
doi:10.1109/JPROC.2009.2037526 Google Scholar

16. Liu, S., J. Zhang, J. Liu, and Q. Yin, "l_1/2,1 group sparse regularization for compressive sensing," Signal, Image and Video Processing, Vol. 10, No. 5, 861-868, 2016.
doi:10.1007/s11760-015-0829-6 Google Scholar

17. Scarnati, T. and A. Gelb, "Accelerated variance based joint sparsity recovery of images from fourier data," arXiv preprint arXiv:1910.08391, 2019. Google Scholar

18. Gelb, A. and T. Scarnati, "Reducing effects of bad data using variance based joint sparsity recovery," Journal of Scientific Computing, Vol. 78, No. 1, 94-120, 2019.
doi:10.1007/s10915-018-0754-2 Google Scholar

19. Güven, H. E., A. Güngör, and M. Cetin, "An augmented Lagrangian method for complex-valued compressed SAR imaging," IEEE Transactions on Computational Imaging, Vol. 2, No. 3, 235-250, 2016.
doi:10.1109/TCI.2016.2580498 Google Scholar

20. Candes, E. J., M. B. Wakin, and S. P. Boyd, "Enhancing sparsity by reweighted l₁ minimization," Journal of Fourier Analysis and Applications, Vol. 14, No. 5, 877-905, 2008.
doi:10.1007/s00041-008-9045-x Google Scholar

21. Giles, D., "The majorization minimization principle and some applications in convex optimization,", Thesis, 2015, doi: 10.15760/honors.175. Google Scholar

22. Archibald, R., A. Gelb, and R. B. Platte, "Image reconstruction from undersampled Fourier data using the polynomial annihilation transform," Journal of Scientic Computing, Vol. 67, No. 2, 432-452, 2016.
doi:10.1007/s10915-015-0088-2 Google Scholar

23. Wang, Y., J. Yang, W. Yin, and Y. Zhang, "A new alternating minimization algorithm for total variation image reconstruction," SIAM Journal on Imaging Sciences, Vol. 1, No. 3, 248-272, 2008.
doi:10.1137/080724265 Google Scholar

24. Duersch, M. I. and D. G. Long, "Analysis of time-domain back-projection for stripmap SAR," International Journal of Remote Sensing, Vol. 36, No. 8, 2010-2036, 2015.
doi:10.1080/01431161.2015.1030044 Google Scholar

25. Ponmani, E. and P. Saravanan, "Image denoising and despeckling methods for SAR images to improve image enhancement performance: A survey," Multimedia Tools and Applications, Vol. 80, No. 17, 26547-26569, 2021.
doi:10.1007/s11042-021-10871-7 Google Scholar

26. Yigit, E., S. Demirci, C. Ozdemir, and M. Tekbas, "Short-range ground-based synthetic aperture radar imaging: Performance comparison between frequency-wavenumber migration and back-projection algorithms," Journal of Applied Remote Sensing, Vol. 7, 073483, 2013.
doi:10.1117/1.JRS.7.073483 Google Scholar

Dr. Nabil Gherbi

Dr. Azzedine Bouaraba

Dr. Mustapha Benssalah

Dr. Aichouche Belhadj Aissa