基于多尺度几何分析的SAR图像降斑方法研究
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摘要
合成孔径雷达(SAR)可以全天时、全天候成像,且具有高分辨和穿透性的优势,在军事和民用方面得到了广泛的应用。但是其中存在大量相干斑严重影响了图像质量并为SAR图像的后续处理增加了困难。因此抑制相干斑噪声是SAR图像至关重要的一个预处理步骤。近年来,小波变换在图像处理中得到广泛应用,但由于它缺少方向选择性,不适宜表示图像边缘、轮廓等线性奇异性的结构特征,为此,一些新的具有多尺度多方向特性的变换应运而生。本文基于非下采样Contourlet变换框架,结合变换SAR图像本身的特点以及相干斑的特性,研究了SAR图像降斑算法,主要工作如下:
1.提出了一种基于邻域方向性信息的SAR图像降斑方法。该算法用本文的改进对数高斯概率密度函数来逼近表示重要信息的子带系数直方图,用两状态的混合指数分布拟合表示不重要信息的子带系数直方图,并设计了各向异性的邻域方向模型,用来捕捉NSCT域SAR图像的方向信息。实验结果表明该方法可以取得较好的SAR图像降斑效果。
2.提出了一种结合数学形态学的SAR图像降斑方法。该方法将形态学的开运算引入到子带掩码的修正中,并根据NSCT本身的多尺度多方向性设定了用于开运算的结构元素。实验结果表明将数学形态学开运算应用到SAR图像降斑,可以在保留图像本身点目标和边缘的同时更好的消除因斑点噪声引起的突变,可以取得更为满意的降斑效果。
3.提出了一种维纳双阈值降斑方法。该方法将小波域的维纳滤波方法引入到NSCT变换域,用广义高斯分布模型拟合图像在NSCT域的分解系数,用于自然图像的去噪,提高了峰值信噪比,本文将该方法进一步推广到SAR图像斑点噪声的抑制,实验结果表明,该方法在去除斑点噪声的同时可以保留更多的边缘信息和图像细节。
Synthetic aperture radar (SAR) images are inherently affected by coherent speckle noise which significantly degrades the image quality and increases great difficulties for SAR image interpretation. Removing noise from SAR image called despeckling is a key pre-processing step for post processing for SAR image. The multi-resolution analysis performed by the wavelet transform has been proved to be a powerful tool. Because of running short of multi-directional character, wavelet transform is not good at describing images’edge and contour, a lot of transform tools with multiscale and multidirectional characteristics are emerged as the times require, such as Curvelet transform, Brushlet transform, Contourlet transform and Nonsubsampled Contourlet transform etc. This paper combining the multiscale and multidirectional characteristics of Nonsubsampled Contourlet transform and the specialty of SAR image speckle noisy, studies SAR image despeckling methods. The innovative points of this paper are as follows:
1. An improved Nonsubsampled Contourlet Transform (NSCT)-based method has been proposed, using subbands mask prior models and directional information for synthetic aperture radar (SAR) image despeckling. We use modified logarithm gaussian distribution to approximate the histogram of coefficients representing important information, and use mixed exponential distribution to fit the histogram of coefficients representing unimportant information. Depending on Bayes principle, we obtained shrinkage factor to modify nonsubsampled Contourlet decomposed coefficients of SAR image. The results show that our shrinkage algorithm achieves great outcome for SAR image despeckling.
2. The second innovation of this paper is to add in the operation of mathematical morphology for despeckling. This method uses mathematical morphological open operation to improve binary masks of subband coefficients and designs special structural element according to multiscale and multidirectional characteristics of nunsubsampled Contourlet transform. The experiment results improve that the decpeckling method combining mathematical morphological removes small abrupt caused by speckle noise, therefore it gets approving despeckled effects.
3. It is an improved Wiener filtering algorithm which is special because of double Wiener filtering and a couple of thresholds for image denoising. We use Generalized Gaussian Distribution (GGD) to fit the histogram of subband coefficients and use the result of the first step of Wiener filtering to estimate coefficient variance which is a significant parameter for the second step of Wiener filtering. The experiment results show that this method improves peak signal to noise ratio for denoised nature image and promotes equivalent number of looks (ENL) for despeckled SAR images.
1.提出了一种基于邻域方向性信息的SAR图像降斑方法。该算法用本文的改进对数高斯概率密度函数来逼近表示重要信息的子带系数直方图,用两状态的混合指数分布拟合表示不重要信息的子带系数直方图,并设计了各向异性的邻域方向模型,用来捕捉NSCT域SAR图像的方向信息。实验结果表明该方法可以取得较好的SAR图像降斑效果。
2.提出了一种结合数学形态学的SAR图像降斑方法。该方法将形态学的开运算引入到子带掩码的修正中,并根据NSCT本身的多尺度多方向性设定了用于开运算的结构元素。实验结果表明将数学形态学开运算应用到SAR图像降斑,可以在保留图像本身点目标和边缘的同时更好的消除因斑点噪声引起的突变,可以取得更为满意的降斑效果。
3.提出了一种维纳双阈值降斑方法。该方法将小波域的维纳滤波方法引入到NSCT变换域,用广义高斯分布模型拟合图像在NSCT域的分解系数,用于自然图像的去噪,提高了峰值信噪比,本文将该方法进一步推广到SAR图像斑点噪声的抑制,实验结果表明,该方法在去除斑点噪声的同时可以保留更多的边缘信息和图像细节。
Synthetic aperture radar (SAR) images are inherently affected by coherent speckle noise which significantly degrades the image quality and increases great difficulties for SAR image interpretation. Removing noise from SAR image called despeckling is a key pre-processing step for post processing for SAR image. The multi-resolution analysis performed by the wavelet transform has been proved to be a powerful tool. Because of running short of multi-directional character, wavelet transform is not good at describing images’edge and contour, a lot of transform tools with multiscale and multidirectional characteristics are emerged as the times require, such as Curvelet transform, Brushlet transform, Contourlet transform and Nonsubsampled Contourlet transform etc. This paper combining the multiscale and multidirectional characteristics of Nonsubsampled Contourlet transform and the specialty of SAR image speckle noisy, studies SAR image despeckling methods. The innovative points of this paper are as follows:
1. An improved Nonsubsampled Contourlet Transform (NSCT)-based method has been proposed, using subbands mask prior models and directional information for synthetic aperture radar (SAR) image despeckling. We use modified logarithm gaussian distribution to approximate the histogram of coefficients representing important information, and use mixed exponential distribution to fit the histogram of coefficients representing unimportant information. Depending on Bayes principle, we obtained shrinkage factor to modify nonsubsampled Contourlet decomposed coefficients of SAR image. The results show that our shrinkage algorithm achieves great outcome for SAR image despeckling.
2. The second innovation of this paper is to add in the operation of mathematical morphology for despeckling. This method uses mathematical morphological open operation to improve binary masks of subband coefficients and designs special structural element according to multiscale and multidirectional characteristics of nunsubsampled Contourlet transform. The experiment results improve that the decpeckling method combining mathematical morphological removes small abrupt caused by speckle noise, therefore it gets approving despeckled effects.
3. It is an improved Wiener filtering algorithm which is special because of double Wiener filtering and a couple of thresholds for image denoising. We use Generalized Gaussian Distribution (GGD) to fit the histogram of subband coefficients and use the result of the first step of Wiener filtering to estimate coefficient variance which is a significant parameter for the second step of Wiener filtering. The experiment results show that this method improves peak signal to noise ratio for denoised nature image and promotes equivalent number of looks (ENL) for despeckled SAR images.
引文
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[2] C.Sherwin,J.Ruina,R.Rawcliff. Some early developments in synthetic aperture radar system.IRE trans.on Military Electronics,vol.6, pp.110-115,1962.
[3] W.Brownm, L.Porcello. An introduction to synthetic aperture radar. IEEE Spectrum,vol.6,pp.52-62,1969.
[4]张澄波.综合孔径雷达:原理、系统分析与应用.北京,科学出版社.
[5] C.A.Wiley. Pulsed Doppler Radar Methods and Apparatus. United States patent, No.3196436, 1965.
[6] C.A.Wiley, Synthetic Aperture Radars-A Paradigm for Technology Evolution. IEEE Trans. AES, 1985, 21:440-443.
[7]李兴东. SAR图像相干斑噪声抑制算法的研究.上海.上海交通大学工学硕士毕业论文.2006.12 .
[8] L.J.Cutrona, et al. A High Resolution Radar Combat-Surveillance System. IRE Trans on Military Electronics.1961,5(2):127-131.
[9] L.J.Cutrona, et al. A Comparison of Techniques for Achieving Fine Azimuth Resolution. IRE Trans on Military Electronics.1962,6(2):129-131.
[10] L.J.Cutrona, et al. On the Application of Coherent Optical Processing Techniques to Synthetic Aperture Radar. Proceedings of IEEE.1966, 54(8).
[11] SIR-B Science Team, Ed. The SIR-B Science Investigations Plan. JPL Pub. 1984,84(3).
[12] R.L. Jordan, B.L. Huneycutt, M. Werner. The Sir-C/X-SAR Synthetic Aperture Radar System. Proc. of IEEE, 1991, 79(6) : 827-838.
[13]李颖. SAR图像斑点抑制算法研究[D].北京航空航天大学, 2002.
[14]郭琦南.多尺度几何分析技术在SAR图像处理中的应用研究.南京.南京航空航天大学工学硕士毕业论文. 2006.2
[15] A. Moreira. Improved Multilook Techniques Applied to SAR and SCANSAR Imagery. IEEE Trans. GRS, vol. 29, pp. 529-534, 1991.
[16] Jingfeng Xiao, Jing Li, A. Moody. A detail-preserving and flexible adaptive filter for speckle suppression in SAR imagery. INT.J. Remote sensing. 2003. Vol. 24, No. 12. 2451–2465.
[17]管鲍,孙洪. SAR图像滤波的迭代方法.电波科学学报.2003. Vol18(1).12-17.
[18]贾承丽,匡纲要. SAR图像去斑方法.中国图像图形学报, 2005,2,10(2).135-141.
[19] Lee.J.S. Digital image enhancement and noise filtering by use of local statistics. IEEE Transactions on Pattern Analysis Machine Intelligence,1980,2(2):165-168.
[20] V. S. Frost, J. A. Stiles, K. S. Shanmugan, J. C. Holtzman. Amodel for radar images and its application to adaptive digital filteringof multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell., vol.PAMI-4, no. 1, pp. 157–166, Jan. 1982.
[21] D.T.Kuan,A.A.Sawchuk, T. C. Strand, and P. Chavel. Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-7, no. 1, pp. 165–177, Jan. 1985.
[22] A.Lopes,R.Touzi and E.Nezry. Adaptive speckle filters and Scene heterogeneity. IEEE Transaction on Geosc.and Remote Sens.,28(6):992-1000,Nov 1990.
[23] Lopes A,Nezry E,Touzi R,et al. Maximum a posteriori filtering and first order texture models in SAR image [A]In:Proceedings of IEEE International Geoscience and Remoe Sensing Symposium[C], Washington,D.C, 1990:2409-2412.
[24] E. J. Candès. Ridgelets: Theory and Applications. Ph.D. Thesis, Technical Report, Department of Statistics, Stanford University, 1998.
[25] E. J. Candès. Monoscale Ridgelets for the Representation of Images with Edges. Technical Report, Department of Statistics, Stanford University, 1999.
[26] J W Goodman. Some Fundamental Properties of Speckle[J]. J. Opt. Soc. Am., 1976, 66 (11), 1145-1150
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