Nonlocal means filtering for polarimetric SAR images based on heterogeneity

Xiaoli XING; Qihao CHEN; Qiao XU; Shuai YANG; Xiuguo LIU

doi:10.11834/jrs.20176114

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Nonlocal means filtering for polarimetric SAR images based on heterogeneity
Vol. 21, Issue 3, Pages: 434-441(2017)
Received：16 May 2016，

Accepted：08 September 2016，

Published：2017-05
DOI： 10.11834/jrs.20176114
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DOI：

Xiaoli XING, Qihao CHEN, Qiao XU, et al. Nonlocal means filtering for polarimetric SAR images based on heterogeneity[J]. Journal of Remote Sensing, 2017, 21(3): 434-441. DOI： 10.11834/jrs.20176114.

摘要

相干斑抑制是极化合成孔径雷达(PolSAR)图像分析的重要预处理步骤。为了更好地抑制极化SAR图像中的相干斑，本文综合目标的异质性和结构信息，提出基于目标异质性的非局部均值滤波方法。首先利用K分布距离度量目标的异质性，并以异质性为基础，保留图像中的点、线等高异质性目标；然后计算图像块之间的异质性差异，最后将其作为度量非局部均值加权滤波像元相似性的权重系数，实现对PolSAR图像的相干斑抑制。实验对比结果表明：本文方法能够有效地抑制相干斑，同时对细节信息和极化信息也具有良好的保持性，能够为后续的图像应用提供支持。

Abstract

Polarimetric Synthetic Aperture Radar (PolSAR) occupies an important place in remote sensing because it provides richer information about the targets and earth surface compared with single-channel SAR systems. However

PolSAR data is contaminated by speckle noise due to the coherent imaging mechanism

which considerably affects the accuracy of target classification and recognition. Therefore

speckle-noise filtering of PolSAR images is a crucial pretreatment. Nonlocal(NL) means compute the weights between two pixels with similar surrounding neighborhoods (known as patches) instead of two individual pixels. Considering that patches contain structural information

the NL mean filter preserves repetitive structures and performs better than other filters. The key point of the NL algorithm is the similarity criterion setting or the patch weights. This paper proposes a technique to reduce speckle noise using NL means by combining structure and homogeneity similarity. First

image heterogeneity is measured based on the distance of K distribution and is further utilized to distinguish homogeneous and heterogeneous regions. In PolSAR imagery

backscattering from point targets is significantly different from that of distributed media. Strong backscattering from point targets is caused by strong elementary scatterers within a resolution cell. They lack the typical characteristics of speckle and are not random in nature. The preservation of signatures from strong point targets and man-made structures is desired for image interpretation and other applications. In this paper

various samples are collected based on scene heterogeneity. A threshold is utilized to preserve the point and line targets. Then

a new strategy is presented to adapt to the changes in the heterogeneity of the image

which sets the weights of the NL means that were implemented between patches based on the heterogeneity coefficient. Finally

the filtered image is computed. The obtained filter is compared with the refined Lee

mean shift

NLLee

and WisNLTV filters. The qualitative and quantitative aspects of the filters were compared. To compare the ability of the filters to maintain details

corresponding areas in the enlarged span images are shown after filtering with various methods. The proposed method is significantly better on the global and local scales than the existing methods. Moreover

results of H/A/α decomposition show that the proposed method effectively converges the same scattering mechanism and retains complicated scattering mechanisms. The quantitative assessment verifies the equivalent number of looks (a measure of noise reduction)

the edge-preserving index

and polarization information preservation on real images. The proposed method has improved filtering performance. The concept of accounting for the heterogeneity coefficient within the NL means algorithm is implemented. The proposed method filters adaptively based on heterogeneity. In addition

comparative results confirm the advantages of the proposed algorithm on both speckle reduction and detail preservation.

关键词

Keywords

references

Buades A, Coll B and Morel J M. 2005. A non-local algorithm for image denoising //Proceedings of 2005 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

陈建宏, 赵拥军, 时银水, 刘伟. 2015. PolSAR快速贝叶斯非局部均值相干斑抑制方法. 西安电子科技大学学报(自然科学版), 42(3): 148–153, 204

Chen J H, Zhao Y J, Shi Y S and Liu W. 2015. Fast bayesian non-local means of polarimetric SAR image despeckling. Journal of Xidian University, 42(3): 148–153, 204

Deledalle C A, Denis L and Tupin F. 2009. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Transactions on Image Processing, 18(12): 2661–2672

Deledalle C A, Denis L, Tupin F, Reigber A and Jager M.2015. NL-SAR: a unified non-local framework for resolution-preserving (Pol)(In)SAR denoising. IEEE Transactions on Geoscience and Remote Sensing, 53(4): 2021–2038

邓少平, 李平湘, 张继贤, 黄国满. 2011. 基于乘积模型的极化SAR滤波. 武汉大学学报(信息科学版), 36(10): 1168–1171

Deng S P, Li P X, Zhang J X and Huang G M. 2011. Filtering of polarimetric SAR imagery based on multiplicative model. Geomatics and Information Science of Wuhan University, 36(10): 1168–1171

Foucher S and Lopez-Martinez C.2014. Analysis, evaluation, and comparison of polarimetric SAR speckle filtering techniques. IEEE Transactions on Image Processing, 23(4): 1751–1764

Lang F K, Yang J, Li D R, Shi L and Wei J J. 2014. Mean-shift-based speckle filtering of polarimetric SAR data.IEEE Transactions on Geoscience and Remote Sensing, 52(7): 4440–4454

Lang F K, Yang J and Li D R. 2015. Adaptive-window polarimetric SAR image speckle filtering based on a homogeneity measurement. IEEE Transactions on Geoscience and Remote Sensing, 53(10): 5435–5446

Lee J S, Hoppel K and Mango S A. 1992. Unsupervised estimation of speckle noise in radar images.International Journal of Imaging Systems and Technology, 4(4): 298–305

Lee J S, Grunes M R and Kwok R. 1994. Classification of multi-look polarimetric SAR imagery based on complex Wishart distribution.International Journal of Remote Sensing, 15(11): 2299–2311

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

Lee J S and Pottier E. 2009. Polarimetric Radar Imaging: from Basics to Applications. Boca Raton: CRC Press of Taylor & Francis Group: 232-246.]

Lee J S, Ainsworth T L, Wang Y T and Chen K S. 2015. Polarimetric SAR speckle filtering and the extended sigma filter. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1150–1160

Li H C, Hong W, Wu Y R and Fan P Z. 2013. Bayesian wavelet shrinkage with heterogeneity-adaptive threshold for SAR image despeckling based on generalized gamma distribution. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2388–2402

Liu G C and Zhong H. 2014. Nonlocal means filter for polarimetric SAR data despeckling based on discriminative similarity measure. IEEE Geoscience and Remote Sensing Letters, 11(2): 514–518

Lopes A, Touzi R and Nezry E. 1990. Adaptive speckle filters and scene heterogeneity. IEEE Transactions on Geoscience and Remote Sensing, 28(6): 992–1000

马晓双, 沈焕锋, 杨杰, 张良培. 2015. 极化SAR相干斑抑制的非局部加权最小均方误差滤波算法. 中国图象图形学报, 20(1): 140–150

Ma X S, Shen H F, Yang J and Zhang L P. 2015. Polarimetric SAR speckle filtering using a nonlocal weighted minimum mean squared error filter. Journal of Image and Graphics, 20(1): 140–150

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

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

Nie X L, Qiao H, Zhang B and Huang X Y. 2016. A nonlocal TV-based variational method for PolSAR data speckle reduction. IEEE Transactions on Image Processing, 25(6): 2620–2634

Novak L M and Burl M C. 1990. Optimal speckle reduction in polarimetric SAR imagery. IEEE Transactions on Aerospace and Electronic Systems, 26(2): 293–305

Torres L, Sant’Anna S J S, da Costa Freitas C and Frery A C.2014. Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means. Pattern Recognition, 47(1): 141–157

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

王超, 张红, 陈曦, 刘智, 闫冬梅. 2008. 全极化合成孔径雷达图像处理. 北京: 科学出版社: 68–69

Wang C, Zhang H, Chen X, Liu Z and Yan D M. 2008. Fully Polarimetric Synthetic Aperture Radar Image Processing. Beijing: Science Press: 68–69

肖世忱, 廖静娟, 沈国状. 2015. 自交叉双边滤波的极化SAR数据相干斑抑制. 遥感学报, 19(3): 400–408

Xiao S C, Liao J J and Shen G Z. 2015. Speckle filtering for polarimetric SAR data based on self-cross bilateral filter. Journal of Remote Sensing, 19(3): 400–408

杨学志, 左美霞, 郎文辉, 张晰, 孟俊敏. 2012. 采用散射特征相似性的极化SAR图像相干斑抑制. 遥感学报, 16(1): 105–115

Yang X Z, Zuo M X, Lang W H, Zhang X and Meng J M. 2012. Speckle reduction for multi-polarimetric SAR image with the similarity of the scattering. Journal of Remote Sensing, 16(1): 105–115

Zhong H, Zhang J J and Liu G C. 2014. Robust polarimetric SAR despeckling based on nonlocal means and distributed lee filter. IEEE Transactions on Geoscience and Remote Sensing, 52(7): 4198–4210

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