SAR image speckle noise suppression algorithm based on background homogeneity and bilateral filtering
- Vol. 25, Issue 5, Pages: 1071-1084(2021)
Published: 07 May 2021
DOI: 10.11834/jrs.20210212
扫 描 看 全 文
浏览全部资源
扫码关注微信
Published: 07 May 2021 ,
扫 描 看 全 文
艾加秋,王非凡,杨兴明,史骏,刘凡.2021.基于背景匀质性双边滤波的SAR图像斑点噪声抑制算法.遥感学报,25(5): 1071-1084
Ai J Q,Wang F F,Yang X M,Shi J and Liu F. 2021. SAR image speckle noise suppression algorithm based on background homogeneity and bilateral filtering. National Remote Sensing Bulletin, 25(5):1071-1084
针对双边滤波在抑制SAR图像相干斑噪声的不足,本文提出了一种基于背景匀质性的改进双边滤波算法BH-IBF(Improved Bilateral Filtering algorithm based on Background Homogeneity ),并将其应用于SAR图像斑点噪声抑制。BH-IBF以传统双边滤波作为基本框架,并利用了双边滤波器中的双边核函数描述像素灰度值之间的相似性以及相邻像素间的几何空间信息。然而,传统双边滤波存在不能有效地滤除强斑点噪声的缺点,并且SAR图像又因成像原理的缺陷导致强斑点噪声普遍存在。针对这些问题,BH-IBF设计了一种根据背景窗口的匀质性进行自适应样本截断的方法,并根据描述背景匀质性的指标自动获取样本截断的截断深度。此外,本文将自适应滤波窗口尺寸以及权重核修改的方案应用到BH-IBF中,以增强匀质区域的斑点噪声平滑强度以及异质区域的边缘信息效果。最后,使用自适应截断后的样本作为已调整权重核的双边滤波器的输入。实验数据显示,BH-IBF能够在有效保留SAR图像纹理信息的同时,获取较好的斑点噪声平滑性能。
As a kind of high-resolution imaging radar
Synthetic Aperture Radar (SAR) plays an important role in civil and military fields because it can realize all-weather and all-weather observation without the limitation of illumination and climate conditions. However
SAR also has limitations. For instance
the SAR image has many speckle noises
which is caused by the principle of coherent imaging that seriously affects the extraction and application of relevant information in the image. Therefore
to make better use of SAR image information
speckle noise reduction is a key step in SAR image processing. Among them
the bilateral filtering algorithm
which combines the geometric domain and the gray-scale domain information filter
is currently the best algorithm in the field of speckle noise removal. In this study
we take the bilateral filtering algorithm as the basic framework and then add corresponding improvement measures in view of the problems and shortcomings of the bilateral filtering algorithm
such as insufficient application of SAR image structure information and difficulty in effectively filtering out strong speckle noise. Finally
we propose an improved bilateral filtering algorithm based on background homogeneity (BH-IBF).
This algorithm aims to effectively remove the speckle noise in the SAR image while retaining the real texture information of the image to the maximum extent. (1) The coefficient of variation is introduced into the weight kernel improvement of the bilateral filtering algorithm
compensating for the problem of the bilateral filter ignoring the structural information of the SAR image to a certain extent; (2) the sample truncation operation is introduced to filter the strong speckle noise in the background area to a certain extent
suppressing the influence of speckle noise
thus effectively solving the bilateral filtering. Strong speckle noise is difficult to filter out; (3) the half width of the background region for the homogeneous region can further improve the smoothness of the image.Taking the simulated and real SAR images intercepted from TerraSAR-X as the experimental objects
the comparison of the filtering effects and evaluation indexes of different filtering algorithms shows the improved results obtained by BH-IBF algorithm
indicating that the proposed algorithm achieves the research objective.
The proposed BH-IBF has a better effect than the traditional filtering algorithm. BH-IBF can not only effectively suppress the speckle noise in the homogeneous region but also protect the edge texture information of the heterogeneous region better
that is
the algorithm can better guarantee the subsequent processing and application of SAR data.
SAR图像斑点噪声抑制改进双边滤波算法自适应样本截断背景匀质性自适应滤波窗口尺寸
SAR image speckle noise reductionimprove bilateral filteringadaptive sample trimminghomogeneity index of the reference windowadaptive window size
Ai J Q, Yang H, Yang X Z, Liu R M, Luo Q W and Zhang X H. 2019. Truncated-statistics-based bilateral filter for speckle reduction in synthetic aperture radar imagery. Journal of Applied Remote Sensing, 13(2): 026505 [DOI: 10.1117/1.JRS.13.026505http://dx.doi.org/10.1117/1.JRS.13.026505]
Alonso-González A, López-Martínez C, Salembier P and Deng X P. 2013. Bilateral distance based filtering for polarimetric SAR data. Remote Sensing, 5(11): 5620-5641 [DOI: 10.3390/rs5115620http://dx.doi.org/10.3390/rs5115620]
Buades A, Coll B and Morel J M. 2008. Nonlocal image and movie denoising. International Journal of Computer Vision, 76(2): 123-139 [DOI: 10.1007/s11263-007-0052-1http://dx.doi.org/10.1007/s11263-007-0052-1]
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 [DOI: 10.1109/TIP.2007.901238http://dx.doi.org/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.2013.2256881http://dx.doi.org/10.1109/JSTARS.2013.2256881]
Feng J D. 2019. Speckle Reduction Algorithms Based on Non-local Means for SAR Images. Dalian: Dalian Maritime University
冯建德. 2019. 基于非局部均值的SAR图像相干斑抑制算法. 大连: 大连海事大学) [DOI: 10.26989/d.cnki.gdlhu.2019.001532http://dx.doi.org/10.26989/d.cnki.gdlhu.2019.001532]
Frost V S, Stiles J A, Shanmugan K S and Holtzman J C. 1982. A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-4(2): 157-166 [DOI: 10.1109/TPAMI.1982.4767223http://dx.doi.org/10.1109/TPAMI.1982.4767223]
Han J W, Kim J H, Cheon S H, Kim J O and Ko S J. 2010. A novel image interpolation method using the bilateral filter. IEEE Transactions on Consumer Electronics, 56(1): 175-181 [DOI: 10.1109/TCE.2010.5439142http://dx.doi.org/10.1109/TCE.2010.5439142]
Hu X. 2019. Despeckling and Segmentation Methods for Synthetic Aperture Radar Images. Xi’an: Xi’an Polytechnic University(胡晓. 2019. SAR图像相干斑抑制与分割方法研究. 西安: 西安工程大学) [DOI: 10.27390/d.cnki.gxbfc.2019.000228]
Jia K. 2019. Research on Nonlocal Mean Filtering Algorithm for PolSAR Image. Tianjin: Civil Aviation University of China(贾锟. 2019. PolSAR图像的非局部均值去噪算法研究. 天津: 中国民航大学) [DOI: 10.27627/d.cnki.gzmhy.2019.000180]
Lee J S. 1981. Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing, 17(1): 24-32 [DOI: 10.1016/s0146-664x(81)80005-6http://dx.doi.org/10.1016/s0146-664x(81)80005-6]
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: 10.1109/36.789635http://dx.doi.org/10.1109/36.789635]
Lee J S, Hoppel K and Mango S A. 1992. Unsupervised estimation of speckle noise in radar images. International Journal of Imaging System and Technology, 4(4): 298-305 [DOI: 10.1002/ima.1850040409http://dx.doi.org/10.1002/ima.1850040409]
Lee J S, Wen J H, Ainsworth T L, Chen K S and Chen A J. 2009. Improved sigma filter for speckle filtering of SAR imagery. IEEE Transactions on Geoscience and Remote Sensing, 47(1): 202-213 [DOI: 10.1109/TGRS.2008.2002881http://dx.doi.org/10.1109/TGRS.2008.2002881]
Li G T and Yu W D. 2012. SAR image despeckling based on adaptive bilateral filter. Journal of Electronics and Information Technology, 34(5): 1076-1081
李光廷, 禹卫东. 2012. 基于自适应Bilateral滤波的SAR图像相干斑抑制. 电子与信息学报, 34(5): 1076-1081 [DOI: 10.3724/SP.J.1146.2011.0092http://dx.doi.org/10.3724/SP.J.1146.2011.0092]
Liu G F. 2014. Feature Extraction and Classification of PolSAR Image. Xi’an: Xidian University
刘高峰. 2014. 极化SAR图像特征提取与分类方法研究. 西安: 西安电子科技大学
Liu L, Yang X Z, Zhou F and Lang W H. 2017. Non-local filtering for polarimetric SAR data based on three dimensional patch matching wavelet transform. Journal of Remote Sensing, 21(2): 218-227
刘留, 杨学志, 周芳, 郎文辉. 2017. 3维块匹配小波变换的极化SAR非局部均值滤波. 遥感学报, 21(2): 218-227 [DOI: 10.11834/jrs.20176257http://dx.doi.org/10.11834/jrs.20176257]
Liu X Y, Qiao T and Qiao Z. 2017. Image enhancement method of mine based on bilateral filtering and Retinex algorithm. Industry and Mine Automation, 43(2): 49-54
刘晓阳, 乔通, 乔智. 2017. 基于双边滤波和Retinex算法的矿井图像增强方法. 工矿自动化, 43(2): 49-54 [DOI: 10.13272/j.issn.1671-251x.2017.02.011http://dx.doi.org/10.13272/j.issn.1671-251x.2017.02.011]
Liu Z H, Miao X G and Tang B Y. 2019. Improvement of SAR image speckle noise suppression algorithm based on wavelet transform. Robotics and Applications, (4): 43-45
刘子豪, 苗新刚, 唐伯雁. 2019. 基于小波变换SAR图像斑噪声抑制算法的改进. 机器人技术与应用, (4): 43-45 [DOI: 10.3969/j.issn.1004-6437.2019.04.015]
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 [DOI: 10.1109/36.62623http://dx.doi.org/10.1109/36.62623]
Niu L. 2019. SAR Image Despeckling and Ship Wake Detection Methods. Shanghai: Shanghai Jiao Tong University
牛林. 2019. SAR图像相干斑抑制及舰船尾迹检测方法研究. 上海: 上海交通大学) [DOI: 10.27307/d.cnki.gsjtu.2019.001900http://dx.doi.org/10.27307/d.cnki.gsjtu.2019.001900]
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, 52(2): 606-616 [DOI: 10.1109/TGRS.2011.2161586http://dx.doi.org/10.1109/TGRS.2011.2161586]
Sica F, Cozzolino D, Zhu X X, Verdoliva L and Poggi G. 2018. InSAR- BM3D: a nonlocal filter for SAR interferometric phase restoration. IEEE Transactions on Geoscience and Remote Sensing, 56(6): 3456-3467 [DOI: 10.1109/TGRS.2018.2800087http://dx.doi.org/10.1109/TGRS.2018.2800087]
Tomasi C and Manduchi R. 1998. Bilateral filtering for gray and color images//Proceedings of the 6th International Conference on Computer Vision. Bombay: IEEE: 839-846 [DOI: 10.1109/ICCV.1998.710815http://dx.doi.org/10.1109/ICCV.1998.710815]
Tong Y B, Zhang Q S and Qi Y P. 2006. Image quality assessing by combining PSNR with SSIM. Journal of Image and Graphics, 11(12): 1758-1763
佟雨兵, 张其善, 祁云平. 2006. 基于PSNR与SSIM联合的图像质量评价模型. 中国图象图形学报, 11(12): 1758-1763 [DOI: 10.3969/j.issn.1006-8961.2006.12.003http://dx.doi.org/10.3969/j.issn.1006-8961.2006.12.003]
Vasile G, Trouvé 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.2005.864142http://dx.doi.org/10.1109/TGRS.2005.864142]
Wang S, Yu J P, Liu K, Hou B and Jiao L C. 2014. Polarimetric SAR speckle reduction based on bilateral filtering. Journal of Radars, 3(1): 35-44
王爽, 于佳平, 刘坤, 侯彪, 焦李成. 2014. 基于双边滤波的极化SAR相干斑抑制. 雷达学报, 3(1): 35-44 [DOI: 10.3724/SP.J.1300.2014.13133http://dx.doi.org/10.3724/SP.J.1300.2014.13133]
Xia J J, Zhang X W and Li W W. 2014. Calculating the surface area of a surface using the simpson integral formula. Science and Technology Information, 12: 238-239
夏军剑, 张新巍, 李维伟. 2014. 利用simpson积分公式计算曲面表面积. 科技资讯, 12(8): 238-239 [DOI: 10.16661/j.cnki.1672-3791.2014.08.002http://dx.doi.org/10.16661/j.cnki.1672-3791.2014.08.002]
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
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 [DOI: 10.11834/jrs.20154117http://dx.doi.org/10.11834/jrs.20154117]
Xing C, Wang J Q and Jia Z Q. 2010. Comparison and analysis of some numerical integration methods. Urban Geotechnical Investigation and Surveying, (1): 104-106
邢诚, 王建强, 贾志强. 2010. 多种数值积分方法比较分析. 城市勘测, (1): 104-106 [DOI: 10.3969/j.issn.1672-8262.2010.01.031]
Xing X L. 2018. Polarimetric SAR Image Speckle Filtering Based on Scene Heterogeneity. Wuhan: China University of Geosciences
行晓黎. 2018. 基于场景异质性的极化SAR相干斑滤波方法. 武汉: 中国地质大学
Yang X Z, Chen J, Zhou F, Lang W H, Zheng X and Li G Q. 2015. Polarimetric SAR image despeckling using non local means filter based on homogeneous pixels preselection. Journal of Electronics and Information Technology, 37(12): 2991-2999
杨学志, 陈靖, 周芳, 郎文辉, 郑鑫, 李国强. 2015. 基于同质像素预选择的极化SAR图像非局部均值滤波. 电子与信息学报, 37(12): 2991-2999 [DOI: 10.11999/JEIT150314http://dx.doi.org/10.11999/JEIT150314]
Yi Z L, Yin D, Hu A Z and Zhang R. 2012. SAR image despeckling based on non-local means filter. Journal of Electronics and Information Technology, 34(4): 950-955
易子麟, 尹东, 胡安洲, 张荣. 2012. 基于非局部均值滤波的SAR图像去噪. 电子与信息学报, 34(4): 950-955 [DOI: 10.3724/SP.J.1146.2011.00918http://dx.doi.org/10.3724/SP.J.1146.2011.00918]
Yu H, Hu H F and Geng Z X. 2019. Image gradient guided window shape adaptive bilateral filtering. Computer Applications and Software, 36(12): 201-208, 213
喻恒, 胡海峰, 耿则勋. 2019. 图像梯度引导的窗口形状自适应双边滤波. 计算机应用与软件, 36(12): 201-208, 213 [DOI: 10.3969/j.issn.1000-386x.2019.12.032http://dx.doi.org/10.3969/j.issn.1000-386x.2019.12.032]
Yu Y J and Acton S T. 2002. Speckle reducing anisotropic diffusion. IEEE Transactions on Image Processing, 11(11): 1260-1270 [DOI: 10.1109/TIP.2002.804276http://dx.doi.org/10.1109/TIP.2002.804276]
Zhang C F. 2019. Image Noise Reduction Based on Wavelet Adaptive Threshold Combined with Bilateral Filtering. Harbin: Harbin University of Science and Technology
张宸枫. 2019. 基于小波自适应阈值结合双边滤波的图像降噪. 哈尔滨: 哈尔滨理工大学
Zhang J J. 2014. Polarimetric SAR Despeckling Research based on Hybrid Patch Similarity. Xi’an: Xidian University
张晶晶. 2014. 基于混合块相似性的极化SAR相干斑抑制研究. 西安: 西安电子科技大学
Zhang M and Gunturk B K. 2008. Multiresolution bilateral filtering for image denoising. IEEE Transactions on Image Processing, 17(12): 2324-2333 [DOI: 10.1109/TIP.2008.2006658http://dx.doi.org/10.1109/TIP.2008.2006658]
Zhang W G, Liu F and Jiao L C. 2009. SAR image despeckling via bilateral filtering. Electronics Letters, 45(15): 781-783 [DOI: 10.1049/el.2009.1591http://dx.doi.org/10.1049/el.2009.1591]
Zhao Z M. 2013. Speckle Filtering for Polarimatric SAR Based on Non-local Means. Zhengzhou: Information Engineering University(赵忠民. 2013. 基于非局部均值的极化SAR相干斑抑制方法研究. 郑州: 解放军信息工程大学)
相关文章
相关作者
相关机构