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Polarimetric radar image despeckling by using similar patches matching and minimum mean square error filter

  • role： First author第一作者
  • Affiliation：

    School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

  • Email：mxs.88@163.com
  • Introduction：马晓双，1988年生，男，讲师，研究方向为极化雷达遥感影像处理与解译。E-mail： mxs.88@163.com
Xiaoshuang MA1,  
  • Affiliation：

    School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China

Penghai WU1,  
  • Affiliation：

    Data and Application Center of High-resolution Earth Observation System of Anhui Province, Hefei 230000, China

Shiyu LIU2,  
  • Affiliation：

    Data and Application Center of High-resolution Earth Observation System of Anhui Province, Hefei 230000, China

Mengyuan YAO2

Resümee

Synthetic Aperture Radar (SAR) systems are capable of capturing images of the earth in day and night and for almost all weather conditions. Polarimetric SAR (PolSAR), which focuses on emitting and receiving complete polarized radar waves to characterize observed targets, is an advanced form of SAR. PolSAR data have unique advantages in obtaining land cover information in comparison with optical remote sensing data, thereby resulting in their wide use in many areas. However, the SAR data are inherently affected by speckle noise. The presence of speckles complicates the PolSAR image interpretation and land surface parameter inversion. Therefore, despeckling is an essential procedure in most cases before using SAR images to obtain land cover information. In the traditional methods based on the linear minimum mean square error (LMMSE) filter, a group of homogeneous image pixels is first selected in a local window to obtain precise filter parameters in the LMMSE estimator. The LMMSE estimator is then generated from the values of the selected pixels and is saved as the filtered value of the pixel being processed. These methods assume that all of the selected pixels are absolutely homogeneous pixels with respect to the processed pixel, which is insufficiently reliable. In addition, most of these methods have limitations, such as a limited selection range of pixels, which are only compared with the characteristics of their own, thereby possibly producing a biased or inferior estimation of the filter parameters in the estimator. In this study, we proposed a similar patch matching and LMMSE filter combined with a PolSAR despeckling method based on the 3D block matching-based algorithm. The main idea behind the proposed method is to select additional similar pixels in the nonlocal area to improve the performance of the LMMSE estimator. The main process of the proposed method is as follows: first, the similar patches in a nonlocal window for each target patch are selected to form a patch group, and the LMMSE filter is used to filter all the pixels in the group. Second, an aggregation step is utilized to estimate the pixels that have been clustered into several groups. The patch matching process is used again to group similar patches by considering the information of the original and basic estimated images. Finally, a collaborated LMMSE filter and an aggregation step are undertaken to filter the image. The experiments on the simulated PolSAR and two real PolSAR images acquired by the GF-3 satellite revealed the positive despeckling performances of the proposed method. The speckle is reduced to a large degree, and the image details, such as the edges and strong point targets, are effectively preserved.

Schlüsselwort

polarimetric synthetic aperture radar;speckle filtering;similar patches matching;linear minimum mean square error filter;nonlocal means

References

  1. 1.
    Alonso-González A, Lopez-Martínez C L and Salembier P. 2012. Filtering and segmentation of polarimetric SAR data based on binary partition trees. IEEE Transactions on Geoscience and Remote Sensing, 50(2): 593–605
  2. 2.
    Buades A, Coll B and Morel J M. 2005. A non-local algorithm for image denoising//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE, 2: 60–65
  3. 3.
    Chen J, Chen Y L, An W T, Cui Y and Yang J. 2011. Nonlocal filtering for polarimetric SAR data: a pretest approach. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1744–1754
  4. 4.
    Chen J H, Zhao Y J, Huang J, Liu W and Lai T. 2014. Improved non-local means filter algorithm of multilook polarimetric SAR image. Journal of Geomatics Science and Technology, 31(5): 496–504 (
  5. 5.
    Cloude S R and Pottier E. 1996. A review of target decomposition theorems in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 34(2): 498–518
  6. 6.
    Conradsen K, Nielsen A A, Schou J and Skriver H. 2003. A test statistic in the complex Wishart distribution and its application to change detection in polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 41(1): 4–19
  7. 7.
    Dabov K, Foi A, Katkovnik V and Egiazarian K. 2007. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Transactions on Image Processing, 16(8): 2080–2095
  8. 8.
    D’Hondt O, Guillaso S and Hellwich O. 2013. Iterative bilateral filtering of polarimetric SAR data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3): 1628–1639
  9. 9.
    Feng H X, Hou B and Gong M G. 2011. SAR image despeckling based on local homogeneous-region segmentation by using pixel-relativity measurement. IEEE Transactions on Geoscience and Remote Sensing, 49(7): 2724–2737
  10. 10.
    Foucher S, Boucher J M and Bénie G B. 2000. Maximum likelihood estimation of the number of looks in SAR images//13th International Conference on Microwaves, Radar and Wireless Communications. Wroclaw: IEEE, 2: 657–660
  11. 11.
    Freeman A and Durden S L. 1998. A three-component scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 36(3): 963–973
  12. 12.
    Goodman N R. 1963. Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction). The Annals of Mathematical Statistics, 34(1): 152–177
  13. 13.
    Lee J S. 1980. Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, 2(2): 165–168
  14. 14.
    Lee J S, Grunes M R and de Grandi G. 1999. Polarimetric SAR speckle filtering and its implication for classification. IEEE Transactions on Geoscience and Remote Sensing, 37(5): 2363–2373
  15. 15.
    Lee J S, Grunes M R, Schuler D L, Pottier E and Ferro-Famil L. 2006. Scattering-model-based speckle filtering of polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 44(1): 176–187
  16. 16.
    Lee J S and Pottier E. 2009. Polarimetric Radar Imaging: from basics to Applications. Boca Raton, FL: CRC Press
  17. 17.
    Lopez-Martínez C and Fabregas X. 2003. Polarimetric SAR speckle noise model. IEEE Transactions on Geoscience and Remote Sensing, 41(10): 2232–2242
  18. 18.
    Ma X S, Shen H F, Zhang L P, Yang J and Zhang H Y. 2015. Adaptive anisotropicdiffusion method for polarimetric SAR speckle filtering. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(3): 1041–1050
  19. 19.
    Nie X L, Qiao H and Zhang B. 2015. A variational model for PolSAR data speckle reduction based on the wishart distribution. IEEE Transactions on Image Processing, 24(4): 1209–1222
  20. 20.
    Novak L M, Burl M C and Irving W W. 1993. Optimal polarimetric processing for enhanced target detection. IEEE Transactions on Aerospace and Electronic Systems, 29(1): 234–244
  21. 21.
    Parrilli S, Poderico M, Angelino C V and Verdoliva L. 2012. A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage. IEEE Transactions on Geoscience and Remote Sensing, 50(2): 606–616
  22. 22.
    Schou J and Skriver H. 2001. Restoration of polarimetric SAR images using simulated annealing. IEEE Transactions on Geoscience and Remote Sensing, 39(9): 2005–2016
  23. 23.
    Touzi R. 2002. A review of speckle filtering in the context of estimation theory. IEEE Transactions on Geoscience and Remote Sensing, 40(11): 2392–2404
  24. 24.
    Van Zyl J J, Zebker H A and Elachi C. 1987. Imaging radar polarization signatures: theory and observation. Radio Science, 22(4): 529–543
  25. 25.
    Vasile G, Trouve E, Lee J S and Buzuloiu V. 2006. Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation. IEEE Transactions on Geoscience and Remote Sensing, 44(6): 1609–1621
  26. 26.
    Yang G H. 2012. Study of polarimetric SAR despeckling based on nonlocal means. Xi’an: Xidian University

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