InSAR影像配准及其并行化算法研究
|
摘要
利用合成孔径雷达干涉测量(InSAR)技术获取地表的高程信息和形变信息已经在地形测绘、地震火山监测、地表沉降和冰川移动等多个领域得到了越来越广泛的关注。在InSAR影像处理中,配准同一地区的两幅或多幅影像是指准确地提取不同影像在同一点的相位信息,是整个InSAR影像处理流程中一个重要的步骤。同时这一步骤处理结果对流程中的后续步骤起着关键的制约作用,通常要达到子像元的配准精度。
本文系统地分析了现有的InSAR影像配准算法的特点,针对InSAR影像自身的特点,研究了二种多级匹配算法。一种是基于相关系数法和最大干涉频谱法的多级配准算法,另一种是基于相关系数法和最小二乘法的多级配准算法。在粗配准过程中,两种算法都利用相关系数法得到像素级的配准结果,为精配准提供了初始点。在精配准过程中,两种算法分别应用最大干涉频谱法和最小二乘法得到了子像素的配准结果,满足了精度要求。
为了证明两种算法的有效性,分别选取不同地形特点的实验区进行实验,达到了满意的配准效果。同时论文进一步从有效性、可靠性、精度、配准质量、计算时间多个角度比较了两种算法的整体性能,得出了一些有益的结论。
针对InSAR影像的大数据量处理,并行处理是一个很好的选择,它充分地利用了现有的计算资源,同时具有很好的扩展性。考虑到处理大数据量的计算效率、计算精度和计算成本等因素,本文构建了一个可靠的、稳健的分布式并行计算环境,即在PC机群上用统一的中间件(MPI)实现了并行系统的构架。
本文以基于相关系数法和最大干涉频谱法的多级配准算法并行化处理为例,在并行系统构架下实现了并行配准算法,得到了较高的计算效率。并通过对并行算法性能的检测,系统分析总结了影响并行处理几个重要因素。
Interferometric Synthetic Aperture Radar (InSAR), as a rising technology in deriving elevation data and slight deformation of earth surface, has drawn extensive attention in various fields such as topographic mapping, monitoring of earthquakes, volcanoes, land subsidence and glacier dynamics and other thematic mapping applications. Registration of two or more images of the same scene is an important procedure in InSAR image processing that seeks to extract differential phase information not obtainable from each one of these images. Meanwhile, the accuracy of this step is crucial to the reliability of subsequent image processing and final results of the data processing chain, meeting subpixel precision request. After analyzing systematically some conventional InSAR registration methods, his paper presents two multi-step image matching algorithms based on the characteristics of InSAR image. One approach is integrating correlation-registration and Max-spectrum image registration, the other is integrating correlation-registration and least square-registration. In coarse mage registration, two approaches all utilize correlation-registration to obtain pixel-accuracy registration results, offering crude control points pairs for the next matching. In precise image registration, two approaches respectively apply Max-spectrum and least square-registration method to register images, the matching accuracy reaches to about subpixel.For purpose of proving validity of the registration method, Experimentations are implemented on several different test sites and meet the request of registration. The paper compares further the two algorithms from some aspects of validity, reliability, registration quality and computing time and obtain some beneficial results.So far, parallel processing is a perfect alternative for InSAR image processing for its capability of fully utilizing available computing resources and high scalability. Considering the requirements of processing highly massive image data in efficiency, accuracy and computation cost, the robust and reliable computation architecture is constrcucted on cluster of PCs by MPI middleware .As a trial on parallel processing, the paper implements a parallized registration algorithm on cluster of PCs and parallel computing environment , showing advantages compared with serial coregistration algorithm. By a few performance criterion tests, several important factors that have greatly effects on parallel system have been analyzed in detail.
本文系统地分析了现有的InSAR影像配准算法的特点,针对InSAR影像自身的特点,研究了二种多级匹配算法。一种是基于相关系数法和最大干涉频谱法的多级配准算法,另一种是基于相关系数法和最小二乘法的多级配准算法。在粗配准过程中,两种算法都利用相关系数法得到像素级的配准结果,为精配准提供了初始点。在精配准过程中,两种算法分别应用最大干涉频谱法和最小二乘法得到了子像素的配准结果,满足了精度要求。
为了证明两种算法的有效性,分别选取不同地形特点的实验区进行实验,达到了满意的配准效果。同时论文进一步从有效性、可靠性、精度、配准质量、计算时间多个角度比较了两种算法的整体性能,得出了一些有益的结论。
针对InSAR影像的大数据量处理,并行处理是一个很好的选择,它充分地利用了现有的计算资源,同时具有很好的扩展性。考虑到处理大数据量的计算效率、计算精度和计算成本等因素,本文构建了一个可靠的、稳健的分布式并行计算环境,即在PC机群上用统一的中间件(MPI)实现了并行系统的构架。
本文以基于相关系数法和最大干涉频谱法的多级配准算法并行化处理为例,在并行系统构架下实现了并行配准算法,得到了较高的计算效率。并通过对并行算法性能的检测,系统分析总结了影响并行处理几个重要因素。
Interferometric Synthetic Aperture Radar (InSAR), as a rising technology in deriving elevation data and slight deformation of earth surface, has drawn extensive attention in various fields such as topographic mapping, monitoring of earthquakes, volcanoes, land subsidence and glacier dynamics and other thematic mapping applications. Registration of two or more images of the same scene is an important procedure in InSAR image processing that seeks to extract differential phase information not obtainable from each one of these images. Meanwhile, the accuracy of this step is crucial to the reliability of subsequent image processing and final results of the data processing chain, meeting subpixel precision request. After analyzing systematically some conventional InSAR registration methods, his paper presents two multi-step image matching algorithms based on the characteristics of InSAR image. One approach is integrating correlation-registration and Max-spectrum image registration, the other is integrating correlation-registration and least square-registration. In coarse mage registration, two approaches all utilize correlation-registration to obtain pixel-accuracy registration results, offering crude control points pairs for the next matching. In precise image registration, two approaches respectively apply Max-spectrum and least square-registration method to register images, the matching accuracy reaches to about subpixel.For purpose of proving validity of the registration method, Experimentations are implemented on several different test sites and meet the request of registration. The paper compares further the two algorithms from some aspects of validity, reliability, registration quality and computing time and obtain some beneficial results.So far, parallel processing is a perfect alternative for InSAR image processing for its capability of fully utilizing available computing resources and high scalability. Considering the requirements of processing highly massive image data in efficiency, accuracy and computation cost, the robust and reliable computation architecture is constrcucted on cluster of PCs by MPI middleware .As a trial on parallel processing, the paper implements a parallized registration algorithm on cluster of PCs and parallel computing environment , showing advantages compared with serial coregistration algorithm. By a few performance criterion tests, several important factors that have greatly effects on parallel system have been analyzed in detail.
引文
[1] Paul Rosen, Scott Hensley, Ian R Joughin, Fuk K Li, Sφren Madsen, Ernesto Rodriguez and Richard Goldstein. Synthetic aperture radar interferometry. Proceedings of the IEEE, 88(3): 333-382, March 2000.
[2] Howard A Zebker, Charles L Werner, Paul A Rosen, and Scott Hensley. Accuracy of topographic maps derived from ERS-1 interferometric data. IEEE Transactions on Geoscience and Remote Sensing, 32(4): 823-836, July 1994.
[3] David W Robinson and Graeme T Reid. Interferogram Analysis, Digital Fringe Pattern Measurement Techniques. Institute of Physics Publishing, Bristol, 1993.
[4] A. J. Wilkinson. Development of an Interferometric SAR Processing Facility Phase2: Implementation of an Image Registration Algorithm. Technical report, UCT Radar Remote Sensing Group, March 1996.
[5] Interferometric SAR Processing for Topographic Mapping.
[6] ANDREW K. Gabriel and RICHARD M.Goldstein, Crossed orbit interferometry: theory and experimental results from SIR-B, Remote Sensing. vol. 9. no.5.857-872. 1988.
[7] V. N. Dvorchenko, Bounds on (deterministic) correlation functions with applications to registration, IEEE Trans. Pattern Anal Machine IntlL, vol. PAMI-5, no. 2, pp. 206-213, 1983.
[8] Parr, G., F. Rottensteiner, and W. Polzleitner, "Image Match Strategy, Digital Image Analysis"(edited by Walter G. Kropatsch and Horst Bischof), Springer, 393-410, 2001.
[9] Hassan Foroosh (Shekarforoush), Josiane B. Zerubia, Extension of Phase Correlation to Subpixel Registration, Zerubia, Senior Member, IEEE, and Marc Berthod, 2002.
[10] Qian Lin, John F Vesecky, Howard A.zebker, Registration of Interferometric SAR Image, Synthetic aperture radar interferometry. Proceedings of the IEEE, 1992.
[11] 廖明生,由InSAR影像高精度自动生成干涉图的关键技术研究,武汉测绘科技大学博士学位论文,2000。
[12] 刘国祥,丁晓利,李志林等,星载SAR复数图像的配准,第30卷,第一期,测绘学报,2001。
[13] 舒宁,雷达影像干涉测量原理 武汉大学出版社2003。
[14] 王超,张红,刘智,星载合成孔径雷达干涉测量,科学出版社,2002。
[15] J. Homer and I.D. Longstaff. Minimising the Co-Registration Window Size for SAR Image Pairs. IEE Proceedings on Radar, Sonar and Navigation, 142(6), December, 1995
[16] Rafael C. Gonalez, Richard E. Woods, Digital Image Processing Second Edition, Pearson Education, Inc., 2002.
[17] A. J. Wilkinson. Development of an Interferometric SAR Processing Facility Phase3: Formation of Interferograms. Technical report, UCT Radar Remote Sensing Group, March 1996.
[18] 张祖勋,张剑清,数字摄影测量学,武汉大学出版社,1997。
[19] M.Jflynn, some computer Organizations and Their Effectiveness. IEEE Trans Computers 21(9): 948～960, 1972.
[20] 孙家旭等《网络并行计算与分布式编程环境》科技出版社,1996。
[21] Shirley A.Williams. Programming models for parallel systems. New York: J.Wiley, 1990.
[22] Al Geist, et al. PVM: A Use' Guide a Tutorial for Networked Parallel Computing. The MIT Press, 1994.
[23] Harry F. Jordan, Gita Alaghband, Fundamental of Parallel Processing, Pearson Education, Inc., 2003.
[24] AMDAHL, G.M, Limits of expection, Int. J. Supercomput. Appl, 2, pp, 88-94, 1988.
[25] GF.Pfister.Clusters of Computers: Characteristics of an Invisible Architecture. IEEE Int'l. Parallel Processing Symp, Honolulu, April 1996.
[26] Al Geist, et al. PVM: A Use' Guide a Tutorial for Networked Parallel Computing. The MIT Press, 1994
[27] Peter S.Pacheco, Parallel Programming with MPI, San Francisco, CA, MorganKaufmann, 1997.
[28] Peter S. Pacheco, A User's Guide to MPI, Department of Mathematics, University of San Francisco, San Francisco, CA 94117, 1998.
[29] Lan Forster, "Designing and Building Parallel Programs", Addison-Wesley, 1995.
[30] David Ashton, William Gropp, and Ewing Lusk, Installation and User's Guide to MPICH, a Portable Implementation of MPI Version 1.2.5, mathematics and computer science division, 1998.
[31] http://www-unix.mcs.anl.gov/mpi/mpich/download.html.
[32] Barry Wilkinson and Michael Allen. Techniques and Applications using Networked Workstations and Parallel Computers. Prentice Hall, Englewood Cliffs, NJ 07632, 1 edition, 1999.
[2] Howard A Zebker, Charles L Werner, Paul A Rosen, and Scott Hensley. Accuracy of topographic maps derived from ERS-1 interferometric data. IEEE Transactions on Geoscience and Remote Sensing, 32(4): 823-836, July 1994.
[3] David W Robinson and Graeme T Reid. Interferogram Analysis, Digital Fringe Pattern Measurement Techniques. Institute of Physics Publishing, Bristol, 1993.
[4] A. J. Wilkinson. Development of an Interferometric SAR Processing Facility Phase2: Implementation of an Image Registration Algorithm. Technical report, UCT Radar Remote Sensing Group, March 1996.
[5] Interferometric SAR Processing for Topographic Mapping.
[6] ANDREW K. Gabriel and RICHARD M.Goldstein, Crossed orbit interferometry: theory and experimental results from SIR-B, Remote Sensing. vol. 9. no.5.857-872. 1988.
[7] V. N. Dvorchenko, Bounds on (deterministic) correlation functions with applications to registration, IEEE Trans. Pattern Anal Machine IntlL, vol. PAMI-5, no. 2, pp. 206-213, 1983.
[8] Parr, G., F. Rottensteiner, and W. Polzleitner, "Image Match Strategy, Digital Image Analysis"(edited by Walter G. Kropatsch and Horst Bischof), Springer, 393-410, 2001.
[9] Hassan Foroosh (Shekarforoush), Josiane B. Zerubia, Extension of Phase Correlation to Subpixel Registration, Zerubia, Senior Member, IEEE, and Marc Berthod, 2002.
[10] Qian Lin, John F Vesecky, Howard A.zebker, Registration of Interferometric SAR Image, Synthetic aperture radar interferometry. Proceedings of the IEEE, 1992.
[11] 廖明生,由InSAR影像高精度自动生成干涉图的关键技术研究,武汉测绘科技大学博士学位论文,2000。
[12] 刘国祥,丁晓利,李志林等,星载SAR复数图像的配准,第30卷,第一期,测绘学报,2001。
[13] 舒宁,雷达影像干涉测量原理 武汉大学出版社2003。
[14] 王超,张红,刘智,星载合成孔径雷达干涉测量,科学出版社,2002。
[15] J. Homer and I.D. Longstaff. Minimising the Co-Registration Window Size for SAR Image Pairs. IEE Proceedings on Radar, Sonar and Navigation, 142(6), December, 1995
[16] Rafael C. Gonalez, Richard E. Woods, Digital Image Processing Second Edition, Pearson Education, Inc., 2002.
[17] A. J. Wilkinson. Development of an Interferometric SAR Processing Facility Phase3: Formation of Interferograms. Technical report, UCT Radar Remote Sensing Group, March 1996.
[18] 张祖勋,张剑清,数字摄影测量学,武汉大学出版社,1997。
[19] M.Jflynn, some computer Organizations and Their Effectiveness. IEEE Trans Computers 21(9): 948～960, 1972.
[20] 孙家旭等《网络并行计算与分布式编程环境》科技出版社,1996。
[21] Shirley A.Williams. Programming models for parallel systems. New York: J.Wiley, 1990.
[22] Al Geist, et al. PVM: A Use' Guide a Tutorial for Networked Parallel Computing. The MIT Press, 1994.
[23] Harry F. Jordan, Gita Alaghband, Fundamental of Parallel Processing, Pearson Education, Inc., 2003.
[24] AMDAHL, G.M, Limits of expection, Int. J. Supercomput. Appl, 2, pp, 88-94, 1988.
[25] GF.Pfister.Clusters of Computers: Characteristics of an Invisible Architecture. IEEE Int'l. Parallel Processing Symp, Honolulu, April 1996.
[26] Al Geist, et al. PVM: A Use' Guide a Tutorial for Networked Parallel Computing. The MIT Press, 1994
[27] Peter S.Pacheco, Parallel Programming with MPI, San Francisco, CA, MorganKaufmann, 1997.
[28] Peter S. Pacheco, A User's Guide to MPI, Department of Mathematics, University of San Francisco, San Francisco, CA 94117, 1998.
[29] Lan Forster, "Designing and Building Parallel Programs", Addison-Wesley, 1995.
[30] David Ashton, William Gropp, and Ewing Lusk, Installation and User's Guide to MPICH, a Portable Implementation of MPI Version 1.2.5, mathematics and computer science division, 1998.
[31] http://www-unix.mcs.anl.gov/mpi/mpich/download.html.
[32] Barry Wilkinson and Michael Allen. Techniques and Applications using Networked Workstations and Parallel Computers. Prentice Hall, Englewood Cliffs, NJ 07632, 1 edition, 1999.