结合相似块匹配及线性最小均方误差滤波器的全极化雷达影像去噪
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摘要
针对全极化合成孔径雷达Pol SAR(Polarimetric Synthetic Aperture Radar)影像相干斑噪声严重的问题,提出了一种结合相似块匹配和线性最小均方误差原理的去噪方法。该方法首先在原始影像上实现相似块组的匹配,进而利用线性最小均方误差滤波器对影像块组进行滤波得到初始去噪结果;然后,同时利用原始影像和初始去噪影像的信息进行相似块组的重新匹配,并再次利用线性最小均方误差原理对重匹配影像块进行去噪,得到影像最终的去噪结果。利用模拟的Pol SAR影像和高分三号卫星Pol SAR影像进行了算法效果的验证。结果表明,去噪算法在显著抑制影像噪声水平的同时,也能较好地保持影像的边缘和极化特性等细节信息。
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(Pol SAR), which focuses on emitting and receiving complete polarized radar waves to characterize observed targets, is an advanced form of SAR. Pol SAR 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 Pol SAR 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 Pol SAR despeckling method based on the 3 D 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 Pol SAR and two real Pol SAR 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.
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(Pol SAR), which focuses on emitting and receiving complete polarized radar waves to characterize observed targets, is an advanced form of SAR. Pol SAR 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 Pol SAR 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 Pol SAR despeckling method based on the 3 D 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 Pol SAR and two real Pol SAR 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.
引文
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[DOI:10.1109/TGRS.2011.2160647]
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[DOI:10.1109/CVPR.2005.38]
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[DOI:10.1109/TGRS.2010.2087763]
Chen J H,Zhao Y J,Huang J,Liu W and Lai T.2014.Improved nonlocal means filter algorithm of multilook polarimetric SAR image.Journal of Geomatics Science and Technology,31(5):496-504(陈建宏,赵拥军,黄洁,刘伟,赖涛.2014.改进的多视Pol SAR非局部均值滤波算法.测绘科学技术学报,31(5):496-504)
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[DOI:10.1109/36.485127]
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[DOI:10.1109/TGRS.2002.808066]
Dabov K,Foi A,Katkovnik V and Egiazarian K.2007.Image denoising by sparse 3-D transform-domain collaborative filtering.IEEETransactions on Image Processing,16(8):2080-2095[DOI:10.1109/TIP.2007.901238]
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[DOI:10.1109/JSTARS.2013.2256881]
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[DOI:10.1109/TGRS.2011.2107915]
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[DOI:10.1109/MIKON.2000.914020]
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[DOI:10.1109/36.673687]
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[DOI:10.1214/aoms/1177704250]
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[DOI:10.1109/TPAMI.1980.4766994]
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[DOI:10.1109/36.789635]
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[DOI:10.1109/TGRS.2005.859338]
Lee J S and Pottier E.2009.Polarimetric Radar Imaging:from basics to Applications.Boca Raton,FL:CRC Press
Lopez-Martínez C and Fabregas X.2003.Polarimetric SAR speckle noise model.IEEE Transactions on Geoscience and Remote Sensing,41(10):2232-2242[DOI:10.1109/TGRS.2003.815240]
Ma X S,Shen H F,Zhang L P,Yang J and Zhang H Y.2015.Adaptive anisotropic diffusion method for polarimetric SAR speckle filtering.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(3):1041-1050[DOI:10.1109/JSTARS.2014.2328332]
Nie X L,Qiao H and Zhang B.2015.A variational model for Pol SARdata speckle reduction based on the wishart distribution.IEEETransactions on Image Processing,24(4):1209-1222[DOI:10.1109/TIP.2015.2396292]
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[DOI:10.1109/7.249129]
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[DOI:10.1109/TGRS.2011.2161586]
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[DOI:10.1109/36.951091]
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[DOI:10.1109/TGRS.2002.803727]
Van Zyl J J,Zebker H A and Elachi C.1987.Imaging radar polarization signatures:theory and observation.Radio Science,22(4):529-543[DOI:10.1029/RS022i004p00529]
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[DOI:10.1109/TGRS.2005.864142]
Yang G H.2012.Study of polarimetric SAR despeckling based on nonlocal means.Xi’an:Xidian University(杨国辉.2012.基于非局部均值的极化SAR降斑研究.西安:西安电子科技大学)
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[DOI:10.1109/CVPR.2005.38]
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[DOI:10.1109/TGRS.2010.2087763]
Chen J H,Zhao Y J,Huang J,Liu W and Lai T.2014.Improved nonlocal means filter algorithm of multilook polarimetric SAR image.Journal of Geomatics Science and Technology,31(5):496-504(陈建宏,赵拥军,黄洁,刘伟,赖涛.2014.改进的多视Pol SAR非局部均值滤波算法.测绘科学技术学报,31(5):496-504)
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[DOI:10.1109/36.485127]
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[DOI:10.1109/TGRS.2002.808066]
Dabov K,Foi A,Katkovnik V and Egiazarian K.2007.Image denoising by sparse 3-D transform-domain collaborative filtering.IEEETransactions on Image Processing,16(8):2080-2095[DOI:10.1109/TIP.2007.901238]
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[DOI:10.1109/JSTARS.2013.2256881]
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[DOI:10.1109/TGRS.2011.2107915]
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[DOI:10.1109/MIKON.2000.914020]
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[DOI:10.1109/36.673687]
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[DOI:10.1214/aoms/1177704250]
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[DOI:10.1109/TPAMI.1980.4766994]
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[DOI:10.1109/36.789635]
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[DOI:10.1109/TGRS.2005.859338]
Lee J S and Pottier E.2009.Polarimetric Radar Imaging:from basics to Applications.Boca Raton,FL:CRC Press
Lopez-Martínez C and Fabregas X.2003.Polarimetric SAR speckle noise model.IEEE Transactions on Geoscience and Remote Sensing,41(10):2232-2242[DOI:10.1109/TGRS.2003.815240]
Ma X S,Shen H F,Zhang L P,Yang J and Zhang H Y.2015.Adaptive anisotropic diffusion method for polarimetric SAR speckle filtering.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,8(3):1041-1050[DOI:10.1109/JSTARS.2014.2328332]
Nie X L,Qiao H and Zhang B.2015.A variational model for Pol SARdata speckle reduction based on the wishart distribution.IEEETransactions on Image Processing,24(4):1209-1222[DOI:10.1109/TIP.2015.2396292]
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[DOI:10.1109/7.249129]
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[DOI:10.1109/TGRS.2011.2161586]
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[DOI:10.1109/36.951091]
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[DOI:10.1109/TGRS.2002.803727]
Van Zyl J J,Zebker H A and Elachi C.1987.Imaging radar polarization signatures:theory and observation.Radio Science,22(4):529-543[DOI:10.1029/RS022i004p00529]
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[DOI:10.1109/TGRS.2005.864142]
Yang G H.2012.Study of polarimetric SAR despeckling based on nonlocal means.Xi’an:Xidian University(杨国辉.2012.基于非局部均值的极化SAR降斑研究.西安:西安电子科技大学)