SAR图像配准以及变化检测的研究
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摘要
合成孔径雷达(SAR)具有全天候、全天时的特点,可方便地获得同一地区不同时段的图像。SAR图像变化检测技术通过对不同时段SAR图像的综合分析,检测同一场景是否有变化发生。SAR图像变化检测已经应用于很多方面,如对人工检测比较困难的热带雨林、沙漠等自然条件恶劣的地区进行实时监测,以了解生态环境变化的情况;对农田进行监测,分析农作物生长状况;对城区环境进行监控,合理规划城市布局,规范土地的使用;对军事目标进行监测,了解兵力部署、军事调动等战场势态,并可用于打击效果评估。
本文研究了合成孔径雷达图像变化检测及相关技术。
首先,介绍SAR图像基本特性。总结了SAR图像与光学图像的区别、SAR图像的统计分布特性;分析了不同SAR图像之间的几何特性,包括平移、缩放、旋转等;阐述了相干斑产生的机理,通过试验比较了几种经典的抑制SAR图像相干斑的算法。
其次,分析SAR图像配准技术。给出图像配准的定义、一般步骤;分析了图像的仿射变换模型;系统归纳了图像配准的常见算法和配准精度评估方法;结合图像能量分布改进提出了一种基于能量分布的SAR图像配准算法;对于强散射目标不明显的SAR图像,本文提出了一种基于不变矩的自动配准法。实验结果证实,这两种方法具有较好的算法稳健性,配准精度较高。
最后,研究SAR图像变化检测算法。介绍SAR图像变化检测的基本过程;总结图像变化检测的基本方法和评估检测效果的一般方法;改进提出了一种基于子图像的主分量分析变化检测算法;另外,对于主次分量不明显的SAR图像,本文提出了一种基于最大似然估计变化检测算法。这两种方法适用范围更广,能够取得很好的检测效果。
Change detection is a process that analyzes a pair of images acquired on the same geographical area at different times in order to identify changes that may have occurred between the considered acquisition dates. The major advantage of synthetic aperture radar (SAR) is its all-weather capability that allows the acquisition of time series of imagery with exact acquisition dates under all climatic conditions. This paper research change detection technology and the related technology using synthetic aperture radar image.
Firstly, the basic characteristics of SAR image are introduced, including the differences between SAR images and optical images, the geometric characteristics,statistical distribution,speckle noise of SAR images, comparing several classic de-speckle noise algorithms by experiments.
Secondly, The definition of image registration is given, the general steps; combination of improved energy distribution of the image is presented based on energy distribution of the SAR image registration; obvious target for the strong scattering of SAR images is proposed based on moment invariant of the automatic registration method. The results confirmed that both methods have good stability of the algorithm, registration accuracy.
Finally, We study the SAR image change detection algorithm. Summarize the basic method of image change detection and assessment of the general method of detecting effect; improvement is presented based on principal component analysis for sub-image change detection algorithm; In addition, this paper proposes a maximum likelihood estimation based on change detection algorithm. Wider application of these two methods can achieve good test results.
本文研究了合成孔径雷达图像变化检测及相关技术。
首先,介绍SAR图像基本特性。总结了SAR图像与光学图像的区别、SAR图像的统计分布特性;分析了不同SAR图像之间的几何特性,包括平移、缩放、旋转等;阐述了相干斑产生的机理,通过试验比较了几种经典的抑制SAR图像相干斑的算法。
其次,分析SAR图像配准技术。给出图像配准的定义、一般步骤;分析了图像的仿射变换模型;系统归纳了图像配准的常见算法和配准精度评估方法;结合图像能量分布改进提出了一种基于能量分布的SAR图像配准算法;对于强散射目标不明显的SAR图像,本文提出了一种基于不变矩的自动配准法。实验结果证实,这两种方法具有较好的算法稳健性,配准精度较高。
最后,研究SAR图像变化检测算法。介绍SAR图像变化检测的基本过程;总结图像变化检测的基本方法和评估检测效果的一般方法;改进提出了一种基于子图像的主分量分析变化检测算法;另外,对于主次分量不明显的SAR图像,本文提出了一种基于最大似然估计变化检测算法。这两种方法适用范围更广,能够取得很好的检测效果。
Change detection is a process that analyzes a pair of images acquired on the same geographical area at different times in order to identify changes that may have occurred between the considered acquisition dates. The major advantage of synthetic aperture radar (SAR) is its all-weather capability that allows the acquisition of time series of imagery with exact acquisition dates under all climatic conditions. This paper research change detection technology and the related technology using synthetic aperture radar image.
Firstly, the basic characteristics of SAR image are introduced, including the differences between SAR images and optical images, the geometric characteristics,statistical distribution,speckle noise of SAR images, comparing several classic de-speckle noise algorithms by experiments.
Secondly, The definition of image registration is given, the general steps; combination of improved energy distribution of the image is presented based on energy distribution of the SAR image registration; obvious target for the strong scattering of SAR images is proposed based on moment invariant of the automatic registration method. The results confirmed that both methods have good stability of the algorithm, registration accuracy.
Finally, We study the SAR image change detection algorithm. Summarize the basic method of image change detection and assessment of the general method of detecting effect; improvement is presented based on principal component analysis for sub-image change detection algorithm; In addition, this paper proposes a maximum likelihood estimation based on change detection algorithm. Wider application of these two methods can achieve good test results.
引文
[1]张辉,王建国.一种基于主分量分析的SAR图像变化检测算法.电子信息学报,2008.
[2]张军团.林君.基于二阶灰度统计特征的SAR图像变化检测.吉林大学学报,2008
[3]张军团.林君.李绚.基于分形维数的SAR图像变化检测.西安工业大学学报,2008
[4]龙红建.傅坤.基于分布模型差异的SAR变化检测,2008
[5]龙红建.詹芊芊.基于优化的星载SAR图像变化检测,2009
[6]张澄波.综合孔径雷达.北京:科学出版社,1989.
[7]陈述彭.遥感地学分析时空维.遥感学报, 1997,1(3):161-169.
[8]张景发,谢礼立,陶夏新.建筑物震害遥感图像的变化检测与震害评估.自然灾害学报, 2002,11(2):59-64.
[9] Peter J.D. Digital Change Detection Techniques: Civilian-and Military Application. Taylar& Fran—cis Ltd. 1999, 231-235.
[10] Singh A. Digital Change Detection Technique Using Remotely-Sensed Data. Int. J. of Remote Sensing, 1989, 10 (6):989-1003.
[11]范海生,马蔼乃,李京.采用图象差值法提取土地利用变化信息方法.遥感学报,2001,5(l):75-80.
[12]刘鹰,张继贤,林宗坚.土地利用动态遥感检测中变化信息提取方法的研究.遥感信息,1999,4(10):21-24.
[13] Y. Bazi, L. Bruzzone, F. Melgani. Automatic Identification of the Number and Values of Decision Thresholds in the Log-Ratio Image for Change Detection in SAR Images. IEEE Geoscience and Remote Sensing Letters,2006,3( 3):349 - 353.
[14]黄勇. SAR图像变化检测及相关技术研究:[博士学位论文],成都,电子科技大学,2006.
[15]张路.基于多元统计分析的遥感影像变化检测方法研究.:[博士学位论文],武汉,武汉大学,2004.
[16] James Donald Anderson. A Comparison of Four Change Detection Techniques for Two Urban Areas in the United States:[Master Dissertation],USA,West Virginia University,2002.
[17]皮亦鸣,杨建宇等.合成孔径雷达成像原理.成都:电子科技大学出版社,2007.
[18]保铮,邢孟道等.雷达成像技术.北京:电子工业出版社,2005.
[19]李江源.高分辨SAR干扰机理与高效干扰方法研究:[博士学位论文],成都,电子科技大学,2007.
[20] Rajalakshmi Menon. K., Balakrishnan. N, Janakiraman. M. Characterization of fluctuating statistics of radar clutter for Indian terrain. IEEE Transaction on Geoscience and Remote Sensing, Mar, 1995, 33 (2):317-323.
[21]姚瑶.高分辨SAR杂波统计特性分析及应用:[硕士学位论文],成都,电子科技大学,2007.
[22] J.M Nicolas, F.Tupin. Gamma mixture modeled with“second kind statistics”:application to SAR image processing. Proceedings of the IGARSS Conference, Toronto. Canada, 2002. 2489-2491.
[23] Henri Maitre.合成孔径雷达图像处理(孙洪).北京:电子工业出版社, 2001.
[24]甘荣兵.合成孔径雷达对抗及目标检测技术研究.:[博士学位论文],成都,电子科技大学,2005.
[25] Zhiyong W, Jixian Z,Tongxiao W. The Contrast Research of the Methods of Restraining the Spackle. ISPRS Congress. Istanbul,2004.110-114.
[26] Armand Lorpes, Ridha Touzi, E.Nezry. Adaptive speckle filters and scene heterogeneity. IEEE Transaction on Geoscience and Remote Sensing, Nov. 1990, 28 (6):992-1000.
[27]郭东华.雷达对地观测理论与应用.北京:科学出版社,2000.
[28] Yunhan Dong, Anthony K Milne, Bruce. C Forster. A review of SAR speckle filters. IEEE International Geoscience and Remote Sensing Symposium, Hawaii, 2000. 633-635.
[29]冈萨雷斯.数字图像处理(MATLAB版).北京:电子工业出版社,2006.
[30] Barbara Zitová,Jan Flusser. Image registration methods: a survey. Image and Vision Computing,2003, 21(11):977-1000.
[31]孙兆林. MATLAB 6.x图像处理.北京:清华大学出版社,2002.
[32]韦燕凤,赵忠明,闫冬梅,曾庆业.基于特征的遥感图像自动配准算法.电子学报,2005, 33 (1):161-165.
[33]陈涛.图像仿射不变特征提取方法研究:[博士学位论文],长沙,国防科学技术大学,2006.
[34] P. Viola, W.M. Wells. Alignment by maximization of mutual information. International Journal of Computer Vision,1997,24:137–154.
[35] J. Flusser, T. Suk. A moment-based approach to registration of images with affine geometric distortion. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32: 382–387.
[36] W.H. Wang, Y.C. Chen. Image registration by control points pairing using the invariant properties of line segments. Pattern Recognition Letters, 1997, 18: 269–281.
[37] A.S. Vasileisky, B. Zhukov, M. Berger. Automated image coregistration based on linear feature recognition. Proceedings of the Second Conference Fusion of Earth Data, Sophia Antipolis, France, 1998, 59–66.
[38]李弼程,罗建书.小波分析及其应用.北京:电子工业出版社,2005.
[39]王超,张红等.星载合成孔径雷达干涉测量.北京:电子工业出版社,2002.
[40]马小虎.一种基于图的平面点集Delaunay三角剖分算法.中国图象图形学报,1997,2(1):7-10.
[41]张晓玲.干涉合成孔径雷达成像处理技术研究.:[博士学位论文],成都,电子科技大学,1999.
[42] Andra.S,O.Al-Kofahi,R.J.Radke. Image Change Detection Algorithms: A Systematic Survey.Technical Report,Department of Electrical. Computer and Systems Engineering, Rensselaer Polytechnic Institute, New York, 2003.
[43] Singh,A.Digital change detection techniques using remotely sensed data. International Journarl of Remote Sensing,1989, 10(6):989-1003.
[44] S. Liu, C. Fu, S. Chang. Statistical change detection with moments under time-varying illumination. IEEE Transaction on Image Processing, September, 1998, 7(9):1258–1268.
[45] Paul L. Rosin, Efstathios Ioannidis. Evaluation of global image thresholding for change detection. Pattern Recognition Letters. 2003,24:2345–2356.
[46] J. Hu, R. Kahsi, D. Lopresti, et al. Why table ground-truthing is hard, in Proc. Sixth Int. Conf. Document Analysis and Recognition, 2001, 129–133.
[47] Qiu B,Prinet V,Perrier E,Monga O. Multi-block PCA method for image change detection. Proceedings of the 12th International Conference on Image Analysis and Processing,Mantova, Italy,17-19, Sept, 2003, 385– 390.
[48]张贤达.矩阵分析与应用.北京:清华大学出版社,2004.
[49] Zhang Shiqing, Lu Hanqing. Learning texture classifier for flooded region detection in SAR images. International Conference on Computer Graphics, Imaging and Vision:New Trends, Beijing, China,26-29 , July, 2005, 93– 98.
[2]张军团.林君.基于二阶灰度统计特征的SAR图像变化检测.吉林大学学报,2008
[3]张军团.林君.李绚.基于分形维数的SAR图像变化检测.西安工业大学学报,2008
[4]龙红建.傅坤.基于分布模型差异的SAR变化检测,2008
[5]龙红建.詹芊芊.基于优化的星载SAR图像变化检测,2009
[6]张澄波.综合孔径雷达.北京:科学出版社,1989.
[7]陈述彭.遥感地学分析时空维.遥感学报, 1997,1(3):161-169.
[8]张景发,谢礼立,陶夏新.建筑物震害遥感图像的变化检测与震害评估.自然灾害学报, 2002,11(2):59-64.
[9] Peter J.D. Digital Change Detection Techniques: Civilian-and Military Application. Taylar& Fran—cis Ltd. 1999, 231-235.
[10] Singh A. Digital Change Detection Technique Using Remotely-Sensed Data. Int. J. of Remote Sensing, 1989, 10 (6):989-1003.
[11]范海生,马蔼乃,李京.采用图象差值法提取土地利用变化信息方法.遥感学报,2001,5(l):75-80.
[12]刘鹰,张继贤,林宗坚.土地利用动态遥感检测中变化信息提取方法的研究.遥感信息,1999,4(10):21-24.
[13] Y. Bazi, L. Bruzzone, F. Melgani. Automatic Identification of the Number and Values of Decision Thresholds in the Log-Ratio Image for Change Detection in SAR Images. IEEE Geoscience and Remote Sensing Letters,2006,3( 3):349 - 353.
[14]黄勇. SAR图像变化检测及相关技术研究:[博士学位论文],成都,电子科技大学,2006.
[15]张路.基于多元统计分析的遥感影像变化检测方法研究.:[博士学位论文],武汉,武汉大学,2004.
[16] James Donald Anderson. A Comparison of Four Change Detection Techniques for Two Urban Areas in the United States:[Master Dissertation],USA,West Virginia University,2002.
[17]皮亦鸣,杨建宇等.合成孔径雷达成像原理.成都:电子科技大学出版社,2007.
[18]保铮,邢孟道等.雷达成像技术.北京:电子工业出版社,2005.
[19]李江源.高分辨SAR干扰机理与高效干扰方法研究:[博士学位论文],成都,电子科技大学,2007.
[20] Rajalakshmi Menon. K., Balakrishnan. N, Janakiraman. M. Characterization of fluctuating statistics of radar clutter for Indian terrain. IEEE Transaction on Geoscience and Remote Sensing, Mar, 1995, 33 (2):317-323.
[21]姚瑶.高分辨SAR杂波统计特性分析及应用:[硕士学位论文],成都,电子科技大学,2007.
[22] J.M Nicolas, F.Tupin. Gamma mixture modeled with“second kind statistics”:application to SAR image processing. Proceedings of the IGARSS Conference, Toronto. Canada, 2002. 2489-2491.
[23] Henri Maitre.合成孔径雷达图像处理(孙洪).北京:电子工业出版社, 2001.
[24]甘荣兵.合成孔径雷达对抗及目标检测技术研究.:[博士学位论文],成都,电子科技大学,2005.
[25] Zhiyong W, Jixian Z,Tongxiao W. The Contrast Research of the Methods of Restraining the Spackle. ISPRS Congress. Istanbul,2004.110-114.
[26] Armand Lorpes, Ridha Touzi, E.Nezry. Adaptive speckle filters and scene heterogeneity. IEEE Transaction on Geoscience and Remote Sensing, Nov. 1990, 28 (6):992-1000.
[27]郭东华.雷达对地观测理论与应用.北京:科学出版社,2000.
[28] Yunhan Dong, Anthony K Milne, Bruce. C Forster. A review of SAR speckle filters. IEEE International Geoscience and Remote Sensing Symposium, Hawaii, 2000. 633-635.
[29]冈萨雷斯.数字图像处理(MATLAB版).北京:电子工业出版社,2006.
[30] Barbara Zitová,Jan Flusser. Image registration methods: a survey. Image and Vision Computing,2003, 21(11):977-1000.
[31]孙兆林. MATLAB 6.x图像处理.北京:清华大学出版社,2002.
[32]韦燕凤,赵忠明,闫冬梅,曾庆业.基于特征的遥感图像自动配准算法.电子学报,2005, 33 (1):161-165.
[33]陈涛.图像仿射不变特征提取方法研究:[博士学位论文],长沙,国防科学技术大学,2006.
[34] P. Viola, W.M. Wells. Alignment by maximization of mutual information. International Journal of Computer Vision,1997,24:137–154.
[35] J. Flusser, T. Suk. A moment-based approach to registration of images with affine geometric distortion. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32: 382–387.
[36] W.H. Wang, Y.C. Chen. Image registration by control points pairing using the invariant properties of line segments. Pattern Recognition Letters, 1997, 18: 269–281.
[37] A.S. Vasileisky, B. Zhukov, M. Berger. Automated image coregistration based on linear feature recognition. Proceedings of the Second Conference Fusion of Earth Data, Sophia Antipolis, France, 1998, 59–66.
[38]李弼程,罗建书.小波分析及其应用.北京:电子工业出版社,2005.
[39]王超,张红等.星载合成孔径雷达干涉测量.北京:电子工业出版社,2002.
[40]马小虎.一种基于图的平面点集Delaunay三角剖分算法.中国图象图形学报,1997,2(1):7-10.
[41]张晓玲.干涉合成孔径雷达成像处理技术研究.:[博士学位论文],成都,电子科技大学,1999.
[42] Andra.S,O.Al-Kofahi,R.J.Radke. Image Change Detection Algorithms: A Systematic Survey.Technical Report,Department of Electrical. Computer and Systems Engineering, Rensselaer Polytechnic Institute, New York, 2003.
[43] Singh,A.Digital change detection techniques using remotely sensed data. International Journarl of Remote Sensing,1989, 10(6):989-1003.
[44] S. Liu, C. Fu, S. Chang. Statistical change detection with moments under time-varying illumination. IEEE Transaction on Image Processing, September, 1998, 7(9):1258–1268.
[45] Paul L. Rosin, Efstathios Ioannidis. Evaluation of global image thresholding for change detection. Pattern Recognition Letters. 2003,24:2345–2356.
[46] J. Hu, R. Kahsi, D. Lopresti, et al. Why table ground-truthing is hard, in Proc. Sixth Int. Conf. Document Analysis and Recognition, 2001, 129–133.
[47] Qiu B,Prinet V,Perrier E,Monga O. Multi-block PCA method for image change detection. Proceedings of the 12th International Conference on Image Analysis and Processing,Mantova, Italy,17-19, Sept, 2003, 385– 390.
[48]张贤达.矩阵分析与应用.北京:清华大学出版社,2004.
[49] Zhang Shiqing, Lu Hanqing. Learning texture classifier for flooded region detection in SAR images. International Conference on Computer Graphics, Imaging and Vision:New Trends, Beijing, China,26-29 , July, 2005, 93– 98.
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