干涉合成孔径雷达测量关键技术研究
|
摘要
干涉合成孔径雷达(InSAR)测量是以合成孔径雷达(SAR)数据为信息源,恢复地表高程信息或变化信息的一项技术。传统的SAR是根据脉冲压缩和合成孔径技术实现距离向和方位向的高分辨,获得观测地面的二维信息。InSAR利用SAR在不同轨道或不同时间对同一地区进行两(多)次观测,分别成像后获得地面同一场景的两(多)幅SAR复图像,完成图像间的配准后,差相位处理产生干涉相位,利用地面点与天线位置之间的几何关系及传感器高度、雷达波长、波束视角、天线基线等参数,精确地测量出图像上每一点的高度信息或变化信息。
为实现星载干涉SAR高程测量的信号处理流程,需重点研究其中的关键技术,即:配准、相位滤波和相位展开技术。本论文的主要工作包括以下几个方面内容: 1.在介绍SAR成像原理和经典成像算法的基础上,指出SAR图像对的距离向和方位向相位偏差,存在于成像后的SAR系统脉冲响应函数的一个线性相位项上。论文中提出了一种基于数据域的配准方法,该方法在成像后点目标的信号模型基础上,通过将其多视处理和数值运算获得相对偏移量,补偿其中一幅图像实现配准。该方法避免了采用幅度或相位配准方法引入的误差,不需要相关处理和插值操作,操作简单,与常规图像配准方法相比,配准一致性更高。
2.现有的SAR图像域的配准方法均采用基于区域(Area-based)的方法。论文中提出一种基于点特征的干涉SAR图像配准方法,通过SAR图像对上匹配的点特征,建立对应关系,修正其中一幅图像实现像元级配准。其中特征提取采用一种改进的多类模板方法;在特征点匹配时,根据同种类型模板检测的特征点才可能匹配的原则,将不同类型的特征点分组,然后计算各组和组间特征点相对距离,并采用组间拟匹配点互相验证策略。在保证像元级配准精度的情况下,该方法有效地减少配准运算量。
3.在上述工作的基础上,提出一种基于曲线拟合的配准方法。此方法利用B-样条函数对边缘检测后主辅二值图像的特征曲线段进行拟合,根据加权矩之差最小准则和相关匹配实现曲线段的匹配,完成各曲线段重心求取后,利用最小二乘法计算两幅图像的整体偏移,修正其中一幅图像实现像元级配准。该方法不仅能够保证像元级的配准精度,同时还具有运算量小、稳健性强等优点。
4.分析指出现有的图像域亚像元级配准方法实质上都是离散搜索方法,由此带来的配准误差无法被消除。为了解决这个问题,论文中提出了一种解析搜索的干涉SAR图像的亚像元级配准方法,该方法构造解析的代价函数,通过优化算法在连续域内搜索亚像元级偏移量,避免使用离散搜索方法引入的配准误差,提高了配准精度,使其在插值意义上达到最优。该方法较经典干涉SAR图像配准方法有着更高的精度。
5.系统研究了平地相位去除、相位滤波、相位展开和高程重建等几个关键环节,其中重点探讨了相位滤波和相位展开问题。在相位滤波方面,实现自适应的方向窗滤波,该方法在有效降噪的同时,避免高相干性区域的图像分辨率的降低。在相位展开方面,介绍了一种合成相位展开方法,该方法利用枝切法在较高质量区域的展开相位精度高、最小二乘法在较低质量区域稳健性强的特点,从较高质量区域向较低质量区域的依次相位展开,恢复出完整且较为平滑的相位曲面。该方法不仅保障较高质量区域的相位展开质量,而且利用较高质量区域估计的边界值较为可靠,使得较低质量区域展开相位的精度得到一定程度的提高。
Interferometric Synthetic Aperture Radar (InSAR) measurement is a technique that reconstructs the terrain elevation or topography change information according to the Synthetic Aperture Radar (SAR) data. Conventional SAR measurement acquires the fine resolution of the range and azimuth directions by using the techniques of pulse compression and matched filtering, respectively. By observing over the same scene at different times or along different tracks, one can obtain the complex images of the same scene on the ground. After the registration of the images, one can get the interferometric phase by subtracting the corresponding phase. There are fixed geometric relation between the object on the ground and spatial geometric framework of the interferometer. On the basis of this relations and some parameters such as platform altitude, radar wavelengh, incidence angle, baseline, and so on, one can accurately get the height and change information of the objects on the observed scene.
This dissertation provides the process of signal processing for spaceborne interferometric SAR, with emphasis on the key techniques of signal processing, such as registration, phase filtering, and phase unwrapping. The main works of the dissertation can be summarized as follows.
1. The principle of SAR imaging and two classic imaging methods are described. Based on it, it is also indicated that phase errors of range and azimuthal directions in SAR image pair is included in a linear phase term of impulse response function for SAR system. According to this conclusion, a new registration method based on data domain is proposed. Based on the signal model of the point target, this method gets the relative offsets by multi-look techniques and numerical operations. After compensating the offsets, one can complete the registration. This method avoids the error inducing by taking amplitude/phase-based registration methods. It does not need to the operations of amplitude correlation and interpolation. Therefore its operation is simple. Compared with conventional image registration methods, the proposed method has better performance.
2. The existing registration methods based on the SAR image all employ area-based method. An image registration method based on the feature information is proposed. This method implements the pixel-level registration by using the mutual features on the image pair. In feature detection, a improved templates method is used to detect the feature. In feature matching, the process is implemented by calculating the relative distances of the feature points. This method reduces the computational load effectively in the case of assuring pixel-level registration accuracy.
3. On the basis of the above method, a registration method of curve fitting is proposed. This method uses B-spline function to fit the feature curve of the image pair after edge detection. The curve matching is implemented by using the principle of weight moments and correlation matching. After finding the centroids of the curves and the whole offsets of the image pair by lease-square method, one can implement the pixel-level registration of image pair. This method has low computational load and excellent robustness.
4. After analyzing the existing subpixel level registration methods, we get the conclusion that they are all essentially discrete search methods. They acquire subpixel level registration accuracy by searching the extremums of the cost function in the interpolated discrete domains. Furthermore, the improvement of registration accuracy is limit and the registration errors cannot be completely eliminated. To overcome the limitations of the discrete search methods, this dissertation proposes a analytic search subpixel registration method for interferometric SAR image. This method searches the subpixel offsets in the continuous domains, which avoids the registration error introduced by the interpolation unit using discrete search methods. The performance of the proposed method has been demonstrated by registering the simulated and real InSAR image pairs. The results of comparison with classic methods are displayed to illuminate the high accuracy of the proposed method.
5. We discuss others key techniques for InSAR measurement, such as flat earth removal, phase filtering, phase unwrapping, and Digital Elevation Model (DEM) reconstruction, with emphysis on the phase filtering and phase unwrapping. In phase filtering, a locally adaptive directional filtering is implemented. This method owns a better performance in fringe preservation and residue reduction. In phase unwrapping, a effective synthetical phase unwrapping method is presented. This method, based on the branch-cut method and finite element method, combines the advantages of the two methods, i.e., in a high-quality region, unwrapped phases retrieved by the branch-cut method are more accurate than those by the lease-square method; in a low-quality region, the robustness of the least-square method is better than that of the branch-cut method. This method assures the phase unwrapping from high-quality region to low-quality region, and resumes the whole and smooth phase map. This method not only assures the accuracy of phase unwrapping in high-quality region, but also improves the accuracy of the phase unwrapping in low-quality region at a certain extent since the boundary value of estimating is reliable from high-quality region.
为实现星载干涉SAR高程测量的信号处理流程,需重点研究其中的关键技术,即:配准、相位滤波和相位展开技术。本论文的主要工作包括以下几个方面内容: 1.在介绍SAR成像原理和经典成像算法的基础上,指出SAR图像对的距离向和方位向相位偏差,存在于成像后的SAR系统脉冲响应函数的一个线性相位项上。论文中提出了一种基于数据域的配准方法,该方法在成像后点目标的信号模型基础上,通过将其多视处理和数值运算获得相对偏移量,补偿其中一幅图像实现配准。该方法避免了采用幅度或相位配准方法引入的误差,不需要相关处理和插值操作,操作简单,与常规图像配准方法相比,配准一致性更高。
2.现有的SAR图像域的配准方法均采用基于区域(Area-based)的方法。论文中提出一种基于点特征的干涉SAR图像配准方法,通过SAR图像对上匹配的点特征,建立对应关系,修正其中一幅图像实现像元级配准。其中特征提取采用一种改进的多类模板方法;在特征点匹配时,根据同种类型模板检测的特征点才可能匹配的原则,将不同类型的特征点分组,然后计算各组和组间特征点相对距离,并采用组间拟匹配点互相验证策略。在保证像元级配准精度的情况下,该方法有效地减少配准运算量。
3.在上述工作的基础上,提出一种基于曲线拟合的配准方法。此方法利用B-样条函数对边缘检测后主辅二值图像的特征曲线段进行拟合,根据加权矩之差最小准则和相关匹配实现曲线段的匹配,完成各曲线段重心求取后,利用最小二乘法计算两幅图像的整体偏移,修正其中一幅图像实现像元级配准。该方法不仅能够保证像元级的配准精度,同时还具有运算量小、稳健性强等优点。
4.分析指出现有的图像域亚像元级配准方法实质上都是离散搜索方法,由此带来的配准误差无法被消除。为了解决这个问题,论文中提出了一种解析搜索的干涉SAR图像的亚像元级配准方法,该方法构造解析的代价函数,通过优化算法在连续域内搜索亚像元级偏移量,避免使用离散搜索方法引入的配准误差,提高了配准精度,使其在插值意义上达到最优。该方法较经典干涉SAR图像配准方法有着更高的精度。
5.系统研究了平地相位去除、相位滤波、相位展开和高程重建等几个关键环节,其中重点探讨了相位滤波和相位展开问题。在相位滤波方面,实现自适应的方向窗滤波,该方法在有效降噪的同时,避免高相干性区域的图像分辨率的降低。在相位展开方面,介绍了一种合成相位展开方法,该方法利用枝切法在较高质量区域的展开相位精度高、最小二乘法在较低质量区域稳健性强的特点,从较高质量区域向较低质量区域的依次相位展开,恢复出完整且较为平滑的相位曲面。该方法不仅保障较高质量区域的相位展开质量,而且利用较高质量区域估计的边界值较为可靠,使得较低质量区域展开相位的精度得到一定程度的提高。
Interferometric Synthetic Aperture Radar (InSAR) measurement is a technique that reconstructs the terrain elevation or topography change information according to the Synthetic Aperture Radar (SAR) data. Conventional SAR measurement acquires the fine resolution of the range and azimuth directions by using the techniques of pulse compression and matched filtering, respectively. By observing over the same scene at different times or along different tracks, one can obtain the complex images of the same scene on the ground. After the registration of the images, one can get the interferometric phase by subtracting the corresponding phase. There are fixed geometric relation between the object on the ground and spatial geometric framework of the interferometer. On the basis of this relations and some parameters such as platform altitude, radar wavelengh, incidence angle, baseline, and so on, one can accurately get the height and change information of the objects on the observed scene.
This dissertation provides the process of signal processing for spaceborne interferometric SAR, with emphasis on the key techniques of signal processing, such as registration, phase filtering, and phase unwrapping. The main works of the dissertation can be summarized as follows.
1. The principle of SAR imaging and two classic imaging methods are described. Based on it, it is also indicated that phase errors of range and azimuthal directions in SAR image pair is included in a linear phase term of impulse response function for SAR system. According to this conclusion, a new registration method based on data domain is proposed. Based on the signal model of the point target, this method gets the relative offsets by multi-look techniques and numerical operations. After compensating the offsets, one can complete the registration. This method avoids the error inducing by taking amplitude/phase-based registration methods. It does not need to the operations of amplitude correlation and interpolation. Therefore its operation is simple. Compared with conventional image registration methods, the proposed method has better performance.
2. The existing registration methods based on the SAR image all employ area-based method. An image registration method based on the feature information is proposed. This method implements the pixel-level registration by using the mutual features on the image pair. In feature detection, a improved templates method is used to detect the feature. In feature matching, the process is implemented by calculating the relative distances of the feature points. This method reduces the computational load effectively in the case of assuring pixel-level registration accuracy.
3. On the basis of the above method, a registration method of curve fitting is proposed. This method uses B-spline function to fit the feature curve of the image pair after edge detection. The curve matching is implemented by using the principle of weight moments and correlation matching. After finding the centroids of the curves and the whole offsets of the image pair by lease-square method, one can implement the pixel-level registration of image pair. This method has low computational load and excellent robustness.
4. After analyzing the existing subpixel level registration methods, we get the conclusion that they are all essentially discrete search methods. They acquire subpixel level registration accuracy by searching the extremums of the cost function in the interpolated discrete domains. Furthermore, the improvement of registration accuracy is limit and the registration errors cannot be completely eliminated. To overcome the limitations of the discrete search methods, this dissertation proposes a analytic search subpixel registration method for interferometric SAR image. This method searches the subpixel offsets in the continuous domains, which avoids the registration error introduced by the interpolation unit using discrete search methods. The performance of the proposed method has been demonstrated by registering the simulated and real InSAR image pairs. The results of comparison with classic methods are displayed to illuminate the high accuracy of the proposed method.
5. We discuss others key techniques for InSAR measurement, such as flat earth removal, phase filtering, phase unwrapping, and Digital Elevation Model (DEM) reconstruction, with emphysis on the phase filtering and phase unwrapping. In phase filtering, a locally adaptive directional filtering is implemented. This method owns a better performance in fringe preservation and residue reduction. In phase unwrapping, a effective synthetical phase unwrapping method is presented. This method, based on the branch-cut method and finite element method, combines the advantages of the two methods, i.e., in a high-quality region, unwrapped phases retrieved by the branch-cut method are more accurate than those by the lease-square method; in a low-quality region, the robustness of the least-square method is better than that of the branch-cut method. This method assures the phase unwrapping from high-quality region to low-quality region, and resumes the whole and smooth phase map. This method not only assures the accuracy of phase unwrapping in high-quality region, but also improves the accuracy of the phase unwrapping in low-quality region at a certain extent since the boundary value of estimating is reliable from high-quality region.
引文
[1] P. A. Rosen, S. Hensley, I. R. Joughin, F. K. Li, S. N. Madsen, E. Rodriguez, and R. M. Goldstein. Synthetic aperture radar interferometry, Proc. IEEE, Mar. 2000, 88(3): 333–382.
[2] R.M. Goldstein, and H.A.Zebker. Interferometric radar measurement of ocean surface currents. Nature, 1987, 328: 707-709.
[3] Rogers A. E, and Ingalls R. P. Venus: Mapping the surface reflectivity by radar interferometry. Science, Aug. 1969, Vol. 165(3895):797-799.
[4] Zisk S. H. A new earth-based radar technique for the measurement of lunar topography. Moon, Sep. 1972, 4(3): 296-306.
[5] Graham L. C. Synthetic interferometer radar for topographic mapping. Proc. IEEE, 1974, 62:763-768.
[6] Zebker H. A., and Goldstein R. M. Topographic mapping from interferometric synthetic aperture radar observations. J. Geophys. Res., April. 1986, 91(B5): 4993-4996.
[7] Goldstein R. M., Zebker H. A., Werner C. L. Satellite radar interferometry: Two-dimensional phase unwrapping. Radio Science, July-Aug. 1988, 23(4): 713-720.
[8] Gabriel A. K., and Goldstein R. M. Crossed orbit interferometry:theory and experiment results from SIRB. Int. J. Remote Sensing, May. 1988, 9(5): 857-872.
[9] Li F, and Goldstein R. M. Studies of multibaseline spaceborne interferometric synthetic aperture radars. IEEE Trans. Geosci. Remote Sensing, Jan. 1990, 28(1): 88-97.
[10] Qian Lin, John F. Vesecky, and Howard A. Zebker, et al. New Approaches in Interferometric SAR Data Processing. IEEE Trans. Geosci. Remote Sensing, May 1992, 30(3): 560–567.
[11] Zebker H. A.and Villasenor J. Decorrelation in Interferometric radar echoes. IEEE Trans. Geosci. Remote Sensing, Sep. 1992, 30(5): 950-959.
[12] Prati C., and Rocca F. Improving Slant-Range Resolution with Mutiple SAR Surveys. IEEE Trans. on AES, Jan.1993, 29(1): 135-143.
[13] Gatelli F., Guamieri A. M., Parizzi F., Pasquali P., Prati C., and Rocca F. The Wavenumber Shift in SAR Interferometry. IEEE Trans. on Geosci. Remote Sensing, July. 1994, 32(4): 855-865.
[14] Zebker H. A., and Wernel C.L. Accuracy of Topographic Maps Derived from ERS-1 Interferometric Radar. IEEE Trans. on Geosci. Remote Sensing, July. 1994, 32(4): 823-836.
[15] Just D, and Bamler R. Phase statistics of interferograms with applications to synthetic aperture radar. Appl. Opt, July. 1994, 33(20): 4361–4368.
[16] Seymour M. S. and Cumming I. G. Maximum likelihood estimation for SAR interferometry. in Proc.IGARSS, Aug. 1994, 2272–2275.
[17] Ghiglia D. C. and Romero L. A. Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods. J. Opt. Soc. Amer A, Jan. 1994, 11(1): 107-177.
[18] Pritt M. D., Shipman J. S. Least-squares two-dimensional phase unwrapping using FFT. IEEE Trans. on Geosci. Remote Sensing, May. 1994, 32(3): 706-708.
[19] Moreira J, Schwabisch M, Fornaro G, et al. X-S AR Interferometry: First Results. IEEE Trans. on Geosci. Remote Sensing, July. 1995, 33(4): 950-955.
[20] Fornaro G, Franceschetti G, and Lanari R. Interferometric SAR Phase Unwrapping Using Green's Formulation. IEEE Trans. on Geosci. Remote Sensing, May. 1996, 34(3): 720-728.
[21] Mrstik V, VanBlaricum G. Terrain Height Measuremnt Accuracy of Interferometric Synthetic Aperture Radars. IEEE Trans. on Geosci. Remote Sensing, Jan. 1996, 34(1): 219-228.
[22] Lanari R., Fornaro G., Riccio D. et al. Generation of Digital Elevation Models by using SIR-C/X-SAR Multifrequency Two-Pass Interferometry: The Etna Case Study. IEEE Trans. on Geosci. Remote Sensing, Jan. 1996, 34(5): 1097-1114.
[23] Touzi. R and Lopes. A. Statistics of the STOKES Parameters and of the Complex Coherence Parameters in One-look and Multilook Speckle Fields. IEEE Trans. on Geosci. Remote Sensing, Mar. 1996, 34(2): 519-531.
[24] Flynn T. J. Two-dimensional phase unwrapping with minimum weighted discontinuity. J. Opt. Soc. Am. A, Oct. 1997, 14(10): 2692-2701.
[25] Fornaro G., Franceschetti G., Lanari R., et al. Interferometric SAR phase unwrapping by using the finite element method. Proc. Inst. Elect. Eng., Radar, Sonar, Navig., Oct. 1997, 144(5): 266–274.
[26] Lee J. S., Papathanassiou P., Ainsworth T. L., Grunes R., and Reigber A. A new technique for noise filtering of SAR interferometric phase images. IEEE Trans. Geosci. Remote Sensing, Sept. 1998, 36(5): 1456–1464.
[27] Bamler R., Adam N., Davidson G. W., and Just D. Noise-induce slope distortion in2-D phase unwrapping by linear estimators with application to SAR interferometry. IEEE Trans. Geosci. Remote Sensing, May. 1998, 36(3): 913-921.
[28] Costantini M. A novel phase unwrapping method based on network programming. IEEE Trans.on Geosci. Remote Sensing., May. 1998, 36(3): 813–821.
[29] Gray A. L., and Farris-Manning P. J. Repeat-pass Interferometry with Airborne Synthetic Aperture Radar. IEEE Trans. Geosci. Remote Sensing, Jan. 1993, 31(1): 180-191.
[30] Wimmer C., Siegmund R., Schw?bisch M., and Moreira J. Generation of High Precision DEMs of the Wadden Sea with Airborne Interferometric SAR IEEE Trans. Geosci. Remote Sensing, Sep. 2000, 38(5): 2234-2245.
[31] 王超,张红,刘智. 星载合成孔径雷达干涉测量. 北京:科学出版社,2002.
[32] 胡庆东.干涉SAR 图像系统相干性和相位展开算法研究.北京航空航天大学 博士学位论文,1998.
[33] 穆冬.干涉合成孔径雷达成像技术研究.南京航空航天大学博士学位论文, 2001.
[34] Lee J. S, Hoppel K. W, and Mango S. A. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery. IEEE Trans. Geosci Remote Sensing, Sep. 1994, 32(5): 1017–1027.
[35] Bamler R, and Hartl P. Synthetic Aperture Radar Interferometry. Inverse Problem, Aug. 1998, 14(4):R1-R54.
[36] N.Marechal. Tomographic formulation of interferometric SAR for terrain elevation mapping. IEEE Trans. Geosci Remote Sensing, May. 1994, 33(3): 726–739.
[37] 张澄波.综合孔径雷达.北京:科学出版社,1989.
[38] 保铮、邢孟道、王彤.《雷达成像技术》. 北京:电子工业出版社,2005.
[39] J. C. Curlander, and R. N. McDonough. Synthetic aperture radar: system and signal processing. Jone Wiley & Sons, INC, 1991.
[40] R. K. Raney, H. Runge, R. Bamler, I. Cumming, and F. Wong. Precision SAR processing without interpolation for range cell migration correction. IEEE Trans. Geosci. Remote Sensing, vol. 32, July 1994.
[41] Moreira A., Mittermayer J., Scheiber R. Extended chirp scaling algorithm for air- and spaceborne SAR data processing in stripmap and scanSAR imaging modes. IEEE Trans. Geosci. Remote Sensing, 1996, 34(5):1123-1139.
[42] R. Scheiber and A. Moreira. Coregistration of interferometric SAR images using spectral diversity. IEEE Trans. Geosci. Remote Sensing, Sep. 2000 38(5): 2179–2191.
[43] A. Moreira and R. Scheiber. A new method for accurate co-registration of interferometric SAR images. in Proc. IGARSS’98, July 1998.
[44] G. Fornaro and G. Franceschetti. Image registration in interferometric SAR processing. Proc. Inst. Elect. Eng. Radar Sonar Nav, Dec. 1995, 142(6): 313–320.
[45] 刘宝泉,冯大政. 数据域的干涉合成孔径雷达配准方法. 已投电子学报。
[46] Bao-quan Liu, Da-zheng Feng, Peng-lang Shui, and Nan Wu. Analytic Search Method for Interferometric SAR Image Registration. IEEE Trans. Geosci. Remote Sensing Letters, Accepted.
[47] X. Dai, S. Khorram. A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Transactions on Geoscience and Remote Sensing, Sep. 1999, 37(5): 2351–2362.
[48] B. Zitova, J. Flusser. Image registration methods: a survey. Image and Vision Computing 21 (2003) 977–1000.
[49] L.G. Brown. A survey of image registration techniques. ACM Computing Surveys,1999, 24(4): 326–376.
[50] J.B.A. Maintz, M.A. Viergever. A survey of medical image registration. Medical Image Analysis, 1998, 2(1): 1–36.
[51] Josien P. W. Pluim, J. B. Antoine Maintz, and Max A. Viergever. Mutual-Information-Based Registration of Medical Images: A Survey. IEEE Trans on Med Imag, Aug. 2003, 22(8): 986-1004.
[52] Hava Lester, Simon R. Arridge. A survey of hierarchical non-linear medical image registration. Pattern Recognition 32 (1999): 129-149.
[53] B.K. Ghaffary, A.A. Sawchuk. A survey of new techniques for image registration and mapping. Proceedings of the SPIE: Applications of Digital Image Processing 432 (1983): 222–239.
[54] T.M. Lehmann, C. G¨ onner, K. Spitzer. Survey: interpolation methods in medical image processing. IEEE Transactions on Medical Imaging 18 (1999): 1049–1075.
[55] G. Stockman, S. Kopstein, S. Benett. Matching images to models for registration and object detection via clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 4 (1982) 229–241.
[56] J. Ton, A.K. Jain. Registering landsat images by point matching. IEEE Transactions on Geoscience and Remote Sensing, Sep. 1989, 27(5): 642–651.
[57] A.D. Ventura, A. Rampini, R. Schettini. Image registration by recognition of corresponding structures. IEEE Transactions on Geoscience and Remote Sensing, May. 1990, 28(3): 305–314.
[58] J. Matas, S. Obdrzalek, O. Chum. Local affine frames for widebaseline stereo. in: R. Kasturi, D. Laurendeau, C. Suen (Eds.), 16th International Conference on Pattern Recognition ICPR 2002, 4: 363–366.
[59] J. Flusser, T. Suk. Degraded image analysis: an invariant approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 1998, 20(6): 590–603.
[60] H. Li, B.S. Manjunath, S.K. Mitra. A contour-based approach to multisensor image registration. IEEE Transactions on Image Processing, Mar. 1995, 4(3): 320–334.
[61] J. le Moigne. Parallel registratin of multi-sensor remotely sensed imagery using wavelet coefficients. Proceedings of the SPIE: Wavelet Applications, Orlando, Florida, 2242, 1994: 432–443.
[62] Giancarlo Rufino, Antonio Moccia, and Salvatore Esposito. DEM Generation by Means of ERS Tandem Data. IEEE Transaction on Geosci. Remote Sensing, Jan 1998, 36(6): 1905-1912.
[63] Adrian Luckman and William Grey. Urban Building Height Variance From Multibaseline ERS Coherence. IEEE Transaction on Geosci. Remote Sensing, Jan. 2003, 41(9): 2022-2025,.
[64] R. Abdelfattah, J. M. Nicolas, and F. Tupin. Interferometric SAR image coregistartion based on the Fourier-Mellin Invariant descriptor. Geoscience and Remote Sensing Symposium, 2002. IGARSS '02, Jun. 2002, 3:1334- 1336.
[65] Harold S. Stone, Michael T. Orchard, Ee-Chien Chang, andStephen A. Martucci. A Fast Direct Fourier-Based Algorithm for Subpixel Registration of Images. IEEE Trans. Geosci. Remote Sensing, Oct. 2001, 39(10): 2235–2243.
[66] Harold S. Stone, and Robert Wolpov. Blind Cross-Spectral Image Registration Using Prefiltering and Fourier-Based Translation Detection. IEEE Trans. Geosci. Remote Sensing, Mar. 2002, 40(3): 637-649.
[67] R.N. Bracewell. The Fourier Transform and Its Applications. McGraw-Hill, New York, 1965.
[68] H. Foroosh, J.B. Zerubia, M. Berthod. Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing, Mar. 2002, 11(3): 188–200.
[69] P.E. Anuta. Spatial registration of multispectral and multitemporal digital imagery using Fast Fourier Transform. IEEE Transactions on Geoscience Electronics, 1970: 353–368.
[70] Jordi Inglada and Alain Giros. On the Possibility of Automatic Multisensor Image Registration. IEEE Trans. Geosci. Remote Sensing, Oct. 2004, 42(10): 2104–2120.
[71] Arlene A. Cole-Rhodes, Kisha L. Johnson, Jacqueline LeMoigne, and Ilya Zavorin. Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient. IEEE Trans. Image Processing, Jan. 1998, 12(12): 27–41.
[72] Hua Xie, Leland E. Pierce, and Fawwaz T. Ulaby. Mutual Information Based Registration of SAR Images. Geoscience and Remote Sensing Symposium, 2003. IGARSS '03, Jul. 2003, 6: 4028-4031.
[73] Hua-Mei Chen, Pramod K. Varshney, and Manoj K. Arora. Performance of Mutual Information Similarity Measure for Registration of Multitemporal Remote Sensing Images. IEEE Trans. Geosci. Remote Sensing, Nov. 2003, 41(11): 2445-2454.
[74] Farzin Mokhtarian and Riku Suomela. Robust Image Corner Detection Through Curvature Scale Space. IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec. 1998, 20(12): 1376–1381.
[75] Zhengwei Yang and Fernand S. Cohen. Image Registration and Object Recognition Using Affine Invariants and Convex Hulls. IEEE Transactions on Image Processing, July. 1999, 8: 934-946.
[76] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8 (1986) 679–698.
[77] Klaus Arbter, Wesley E. Snyder, Hans Burkhardt. Application of affine-invariant fourier descriptors to recognition of 3-D objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, Jul. 1990,12(7): 640-647.
[78] M. J. Paulik, M. Das, and N. K. Loh. Nonstationary autoregressive modeling of object contours. IEEE Trans. Signal Processing, 1992,40(3):660-675.
[79] F. S. Cohen, Z. Huang, and Z. Yang. Invariant matching and identification of curves using B-splines curve representation. IEEE Trans. Image Processing, Jan. 1995, 4(1): 1-10.
[80] Zhaohui Huang and Fernand S. Cohen. Affine-Invariant B-Spline moments for curve matching. IEEE Trans. Image Processing, Oct. 1996, 10(10):1473-1480.
[81] Minghui Xia,and Bede Liu. Image registration by “Super-Curves”. IEEE Trans. Image Processing, May. 2004, 13(5):720-732.
[82] 刘宝泉,冯大政,武楠,李军侠.基于点特征的干涉合成孔径雷达(InSAR) 复图像自动配准算法. 航空学报,2007, 28(1):161-166.
[83] 刘宝泉,冯大政,李军侠,武楠. 基于曲线拟合的干涉合成孔径雷达(InSAR) 复图像自动配准算法.电子与信息学报,2007, 29(7):1666-1669.
[84] 刘宝泉,冯大政,武楠,李军侠. 干涉合成孔径雷达复图像自动配准算法.西安电子科技大学学报,2006,33(6): 887-891.
[85] A.L. Gray, K.E. Mattar, and P.W. Vachon. InSAR Results from the RADARSAT Antarctic Mapping Mission Data: Estimation of Glacier Motion using a Simple Registration Procedure. Geoscience and Remote Sensing Symposium Proceedings, Jul. 1998, 3: 1638-1640.
[86] Q. Lin, J. F. Vesecky, and H. A. Zebker. Registration of interferometric SAR images. in Proc. IGARSS, Houston, TX, May. 1992: 1579–1581.
[87] P. Stoica, Y. Selen. Cyclic minimizers, majorization techniques, and the expectation maximization algorithm: a refresher. IEEE Signal Processing Magazine, Jan. 2004, 21(1): 112-114.
[88] J. H. Mathews, K. D. Fink. Numerical Methods Using MATLAB. Third Edition, Publishing House of Electronics Industry.
[89] Epsilon Nought, Radar Remote Sensing. Sample data. [Online]. Available: http://epsilon.nought.de/.
[90] Trouvé E., Nicolas J. M., and Ma?tre H. Improving phase unwrapping techniques by the use of local frequency estimates. IEEE Trans. Geosci. Remote Sensing, November. 1998, 36(6): 1963-1972.
[91] Goldstein R. M. and Werner C. L. Radar interferogram filtering for geophysical applications. Geophys. Res. Lett., Mar. 1998, 25(21): 4035–4038.
[92] López-Martínez C., and Fàbregas X. Modeling and Reduction of SAR Interferometric Phase Noise in the Wavelet Domain. IEEE Trans. Geosci. Remote Sensing, Dec. 2002, 40(12): 2553–2566.
[93] Suksmono A. B., and Hirose A. Adaptive Noise Reduction of InSAR Images Based a Complex-Valued MRF Model and Its Application to Phase Unwrapping Problem. IEEE Trans. Geosci. Remote Sensing, March. 2002, 40(3): 699–709.
[94] Lorenzo-Ginori J. V., Plataniotis K. N., and Venetsanopoulos A. N. Nonlinear filtering for phase image denoising. IEE Proc-Vis. Image Signal Process., Oct. 2002, 149(5): 290-296.
[95] Wu N, Feng D. Z, and Li J. X. A Locally Adaptive Filter of Interferometric Phase Images. IEEE Trans. Geosci. Remote Sensing Letters, Jan. 2006, 3(1): 73–77.
[96] Spagnolini U. 2-D phase unwrapping and instantaneous frequency estimation. IEEE Trans. Geosci. Remote Sensing, Mar. 1995, 33(3): 579-589.
[97] Fornaro G., Franceschetti G., and Lanari R. Interferometric SAR Phase Unwrapping Using Green's Formulation. IEEE Trans. on Geosci. Remote Sensing, May. 1996, 34(3): 720-728.
[98] Fornaro G., Franceschetti G. Robust phase unwrapping techniques: A Comparison. J. Opt. Soc. Am. A, Dec. 1996, 13(12): 2355-2366.
[99] Ghiglia D. C., and Romero L. A. Minimum LP-norm two-dimensional phase unwrapping. J. Opt. Soc. Amer A, Oct. 1996, 13(10): 1999-2013.
[100] Fornaro G., Sansosti E. A two-dimensional region growing least squares phase unwrapping algorithm for interferometric SAR processing. IEEE Trans. on Geosci. Remote Sensing, Sep. 1999, 37(5): 2215-2226.
[101] Xu W., and Cumming I. A region growing algorithm for InSAR phase unwrapping. IEEE Trans. on Geosci. Remote Sensing, Jan. 1999, 37(1): 124-134.
[102] Chen C. W., and Zebker H. A. Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms. J. Opt. Soc. Amer A, 2000, 17(3): 401-414.
[103] Carballo G. F., and Fieguth P. W. Probabilistic Cost Functions for Network Flow Phase Unwrapping. IEEE Trans. on Geosci. Remote Sensing, Sep. 2000, 38(5): 2192-2201.
[104] Chen, C.W., and Zebker, H.A. Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization. Journal of the Optical Society of America A, Feb. 2001, 18(2): 338-51.
[105] Trouv′e E., Caramma M., and Ma?ytre H. Fringe detection in noisy complex interferograms. Appl. Opt., July. 1996, 35(20): 3799–3806.
[106] 李笑郁、毛士艺. 分支阻断干涉 SAR 相位展开算法的解析与实现. 电子学报,2001 年 12 月,12(z1): 1790-1793.
[107] Pritt, M. D. Phase unwrapping by means of multigrid techniques forinterferometric SAR. IEEE Trans. on Geosci. Remote Sensing, May. 1996, 34(3): 728-738.
[108] 武楠,冯大政,刘宝泉.一种基于枝切法和有限元法的干涉SAR合成相位展开方法. 电子与信息学报,2007, 29(4): 846-850.
[109] Ghiglia D. C., Pritt M. D., Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software. New York: Wiley, 1998: 74-76.
[110] Kwon Y. W., Bang H., The Finite Element Method Using MATLAB, New York: CRC, 2000: 85-86.
[111] 张贤达著. 现代信号处理. 清华大学出版社,第二版,2002年.
[112] Giovanni Corsini, Marco Diani, Fabrizio Lombardini, and Gianpaolo Pinelli. Simulated Analysis and Optimization of a Three-Antenna Airborne InSARSystem for Topographic Mapping. IEEE Trans on Geosci and Remote Sensing, Sep. 1999, 37(5): 2518-2529.
[113] Lombardini F. Absolute Phase Retrieval in a Three-element Synthetic Aperture Radar Interferometer. Proc.1996 CIE International Conference of Radar, Beijing, China, 8th-10th October. 1996: 309?312.
[114] Ghiglia D C, Wahl D E. Interferometric Synthetic Aperture Radar Terrain Elevation Mapping from Multiple Observations. IEEE Digital Signal Processing Workshop, 1994: 33?36.
[115] F. Lombardini, F. Gini, and P. Matteucci. Application of Array Processing Techniques to Multibaseline InSAR for Layover Solution. Radar Conference, 2001: 210 – 215.
[116] Fabrizio Lombardini and Fulvio Gini. Multiple Reflectivities Estimation for Multibaseline InSAR Imaging of Layover Extended Sources. Radar Conference, Proceedings of the International, 2003: 257 – 263.
[117] Pierfrancesco Lombardo, Fabrizio Lombardini. Multi-baseline SAR interferometry for Terrain Slope Adaptivity. Radar Conference, IEEE National 1997:196 – 201.
[118] Lei Ying, David . Munson, RalfKoetter, Brendan J. Multibaseline InSAR Terrain Elevation Estimation: A Dynamic Programming Approach. Image Processing, Proceedings, 2003: 157-160.
[119] J. J. van Zyl and R. Chellappa. Bayesian classification of polarimetric SAR images using adaptive a priori probabilities. Int. J. Remote sens. 1992:835-840.
[120] S. R. Cloude and E. Pottier. A review of target decomposition theorems in radar polarimetry. IEEE Trans on Geosci and Remote Sensing, 1996, 34: 498-518.
[121] D. L. Schuler, J. S. Lee, and G. De Grandi. Measurement of topography using polarimetric SAR images. IEEE Trans on Geosci and Remote Sensing, 1996, 34: 1266-1277.
[122] S. R. Cloude, and K. P. Papathanassiou. polarmetric SAR interferometry. IEEE Trans on Geosci and Remote Sensing, Sep. 1998. 36(5): 1551-1565.
[123] K. P. Papathanassiou and A. Reigber. On the interferometric coherence: A multifrequency and multitemporal analysis. In Proc. FRINGE’96 workshop. ESA, Zurich, Switzerland, 1996: 319-330.
[124] R. N. Treuhaft, S. N. Madsen, and M. Moghaddam. Vegetation characteristics and underlying topography from interferometric data. Radio Sci, 1996:1449-1495.
[125] J. O. Hagberg, L. M. Ulander, and J. Askne. Repeat-pass SAR interferometry over forested terrain. IEEE Trans on Geosci and Remote Sensing, Mar. 1995, 33(2): 331-340.
[126] J. Askne, P. B. Dammert, L. M. Ulander, and G. Smith. C-band repeat-pass interferometric SAR observations of the forest. IEEE Trans on Geosci and Remote Sensing, Jan 1997, 35(1): 331-340.
[2] R.M. Goldstein, and H.A.Zebker. Interferometric radar measurement of ocean surface currents. Nature, 1987, 328: 707-709.
[3] Rogers A. E, and Ingalls R. P. Venus: Mapping the surface reflectivity by radar interferometry. Science, Aug. 1969, Vol. 165(3895):797-799.
[4] Zisk S. H. A new earth-based radar technique for the measurement of lunar topography. Moon, Sep. 1972, 4(3): 296-306.
[5] Graham L. C. Synthetic interferometer radar for topographic mapping. Proc. IEEE, 1974, 62:763-768.
[6] Zebker H. A., and Goldstein R. M. Topographic mapping from interferometric synthetic aperture radar observations. J. Geophys. Res., April. 1986, 91(B5): 4993-4996.
[7] Goldstein R. M., Zebker H. A., Werner C. L. Satellite radar interferometry: Two-dimensional phase unwrapping. Radio Science, July-Aug. 1988, 23(4): 713-720.
[8] Gabriel A. K., and Goldstein R. M. Crossed orbit interferometry:theory and experiment results from SIRB. Int. J. Remote Sensing, May. 1988, 9(5): 857-872.
[9] Li F, and Goldstein R. M. Studies of multibaseline spaceborne interferometric synthetic aperture radars. IEEE Trans. Geosci. Remote Sensing, Jan. 1990, 28(1): 88-97.
[10] Qian Lin, John F. Vesecky, and Howard A. Zebker, et al. New Approaches in Interferometric SAR Data Processing. IEEE Trans. Geosci. Remote Sensing, May 1992, 30(3): 560–567.
[11] Zebker H. A.and Villasenor J. Decorrelation in Interferometric radar echoes. IEEE Trans. Geosci. Remote Sensing, Sep. 1992, 30(5): 950-959.
[12] Prati C., and Rocca F. Improving Slant-Range Resolution with Mutiple SAR Surveys. IEEE Trans. on AES, Jan.1993, 29(1): 135-143.
[13] Gatelli F., Guamieri A. M., Parizzi F., Pasquali P., Prati C., and Rocca F. The Wavenumber Shift in SAR Interferometry. IEEE Trans. on Geosci. Remote Sensing, July. 1994, 32(4): 855-865.
[14] Zebker H. A., and Wernel C.L. Accuracy of Topographic Maps Derived from ERS-1 Interferometric Radar. IEEE Trans. on Geosci. Remote Sensing, July. 1994, 32(4): 823-836.
[15] Just D, and Bamler R. Phase statistics of interferograms with applications to synthetic aperture radar. Appl. Opt, July. 1994, 33(20): 4361–4368.
[16] Seymour M. S. and Cumming I. G. Maximum likelihood estimation for SAR interferometry. in Proc.IGARSS, Aug. 1994, 2272–2275.
[17] Ghiglia D. C. and Romero L. A. Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods. J. Opt. Soc. Amer A, Jan. 1994, 11(1): 107-177.
[18] Pritt M. D., Shipman J. S. Least-squares two-dimensional phase unwrapping using FFT. IEEE Trans. on Geosci. Remote Sensing, May. 1994, 32(3): 706-708.
[19] Moreira J, Schwabisch M, Fornaro G, et al. X-S AR Interferometry: First Results. IEEE Trans. on Geosci. Remote Sensing, July. 1995, 33(4): 950-955.
[20] Fornaro G, Franceschetti G, and Lanari R. Interferometric SAR Phase Unwrapping Using Green's Formulation. IEEE Trans. on Geosci. Remote Sensing, May. 1996, 34(3): 720-728.
[21] Mrstik V, VanBlaricum G. Terrain Height Measuremnt Accuracy of Interferometric Synthetic Aperture Radars. IEEE Trans. on Geosci. Remote Sensing, Jan. 1996, 34(1): 219-228.
[22] Lanari R., Fornaro G., Riccio D. et al. Generation of Digital Elevation Models by using SIR-C/X-SAR Multifrequency Two-Pass Interferometry: The Etna Case Study. IEEE Trans. on Geosci. Remote Sensing, Jan. 1996, 34(5): 1097-1114.
[23] Touzi. R and Lopes. A. Statistics of the STOKES Parameters and of the Complex Coherence Parameters in One-look and Multilook Speckle Fields. IEEE Trans. on Geosci. Remote Sensing, Mar. 1996, 34(2): 519-531.
[24] Flynn T. J. Two-dimensional phase unwrapping with minimum weighted discontinuity. J. Opt. Soc. Am. A, Oct. 1997, 14(10): 2692-2701.
[25] Fornaro G., Franceschetti G., Lanari R., et al. Interferometric SAR phase unwrapping by using the finite element method. Proc. Inst. Elect. Eng., Radar, Sonar, Navig., Oct. 1997, 144(5): 266–274.
[26] Lee J. S., Papathanassiou P., Ainsworth T. L., Grunes R., and Reigber A. A new technique for noise filtering of SAR interferometric phase images. IEEE Trans. Geosci. Remote Sensing, Sept. 1998, 36(5): 1456–1464.
[27] Bamler R., Adam N., Davidson G. W., and Just D. Noise-induce slope distortion in2-D phase unwrapping by linear estimators with application to SAR interferometry. IEEE Trans. Geosci. Remote Sensing, May. 1998, 36(3): 913-921.
[28] Costantini M. A novel phase unwrapping method based on network programming. IEEE Trans.on Geosci. Remote Sensing., May. 1998, 36(3): 813–821.
[29] Gray A. L., and Farris-Manning P. J. Repeat-pass Interferometry with Airborne Synthetic Aperture Radar. IEEE Trans. Geosci. Remote Sensing, Jan. 1993, 31(1): 180-191.
[30] Wimmer C., Siegmund R., Schw?bisch M., and Moreira J. Generation of High Precision DEMs of the Wadden Sea with Airborne Interferometric SAR IEEE Trans. Geosci. Remote Sensing, Sep. 2000, 38(5): 2234-2245.
[31] 王超,张红,刘智. 星载合成孔径雷达干涉测量. 北京:科学出版社,2002.
[32] 胡庆东.干涉SAR 图像系统相干性和相位展开算法研究.北京航空航天大学 博士学位论文,1998.
[33] 穆冬.干涉合成孔径雷达成像技术研究.南京航空航天大学博士学位论文, 2001.
[34] Lee J. S, Hoppel K. W, and Mango S. A. Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery. IEEE Trans. Geosci Remote Sensing, Sep. 1994, 32(5): 1017–1027.
[35] Bamler R, and Hartl P. Synthetic Aperture Radar Interferometry. Inverse Problem, Aug. 1998, 14(4):R1-R54.
[36] N.Marechal. Tomographic formulation of interferometric SAR for terrain elevation mapping. IEEE Trans. Geosci Remote Sensing, May. 1994, 33(3): 726–739.
[37] 张澄波.综合孔径雷达.北京:科学出版社,1989.
[38] 保铮、邢孟道、王彤.《雷达成像技术》. 北京:电子工业出版社,2005.
[39] J. C. Curlander, and R. N. McDonough. Synthetic aperture radar: system and signal processing. Jone Wiley & Sons, INC, 1991.
[40] R. K. Raney, H. Runge, R. Bamler, I. Cumming, and F. Wong. Precision SAR processing without interpolation for range cell migration correction. IEEE Trans. Geosci. Remote Sensing, vol. 32, July 1994.
[41] Moreira A., Mittermayer J., Scheiber R. Extended chirp scaling algorithm for air- and spaceborne SAR data processing in stripmap and scanSAR imaging modes. IEEE Trans. Geosci. Remote Sensing, 1996, 34(5):1123-1139.
[42] R. Scheiber and A. Moreira. Coregistration of interferometric SAR images using spectral diversity. IEEE Trans. Geosci. Remote Sensing, Sep. 2000 38(5): 2179–2191.
[43] A. Moreira and R. Scheiber. A new method for accurate co-registration of interferometric SAR images. in Proc. IGARSS’98, July 1998.
[44] G. Fornaro and G. Franceschetti. Image registration in interferometric SAR processing. Proc. Inst. Elect. Eng. Radar Sonar Nav, Dec. 1995, 142(6): 313–320.
[45] 刘宝泉,冯大政. 数据域的干涉合成孔径雷达配准方法. 已投电子学报。
[46] Bao-quan Liu, Da-zheng Feng, Peng-lang Shui, and Nan Wu. Analytic Search Method for Interferometric SAR Image Registration. IEEE Trans. Geosci. Remote Sensing Letters, Accepted.
[47] X. Dai, S. Khorram. A feature-based image registration algorithm using improved chain-code representation combined with invariant moments. IEEE Transactions on Geoscience and Remote Sensing, Sep. 1999, 37(5): 2351–2362.
[48] B. Zitova, J. Flusser. Image registration methods: a survey. Image and Vision Computing 21 (2003) 977–1000.
[49] L.G. Brown. A survey of image registration techniques. ACM Computing Surveys,1999, 24(4): 326–376.
[50] J.B.A. Maintz, M.A. Viergever. A survey of medical image registration. Medical Image Analysis, 1998, 2(1): 1–36.
[51] Josien P. W. Pluim, J. B. Antoine Maintz, and Max A. Viergever. Mutual-Information-Based Registration of Medical Images: A Survey. IEEE Trans on Med Imag, Aug. 2003, 22(8): 986-1004.
[52] Hava Lester, Simon R. Arridge. A survey of hierarchical non-linear medical image registration. Pattern Recognition 32 (1999): 129-149.
[53] B.K. Ghaffary, A.A. Sawchuk. A survey of new techniques for image registration and mapping. Proceedings of the SPIE: Applications of Digital Image Processing 432 (1983): 222–239.
[54] T.M. Lehmann, C. G¨ onner, K. Spitzer. Survey: interpolation methods in medical image processing. IEEE Transactions on Medical Imaging 18 (1999): 1049–1075.
[55] G. Stockman, S. Kopstein, S. Benett. Matching images to models for registration and object detection via clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 4 (1982) 229–241.
[56] J. Ton, A.K. Jain. Registering landsat images by point matching. IEEE Transactions on Geoscience and Remote Sensing, Sep. 1989, 27(5): 642–651.
[57] A.D. Ventura, A. Rampini, R. Schettini. Image registration by recognition of corresponding structures. IEEE Transactions on Geoscience and Remote Sensing, May. 1990, 28(3): 305–314.
[58] J. Matas, S. Obdrzalek, O. Chum. Local affine frames for widebaseline stereo. in: R. Kasturi, D. Laurendeau, C. Suen (Eds.), 16th International Conference on Pattern Recognition ICPR 2002, 4: 363–366.
[59] J. Flusser, T. Suk. Degraded image analysis: an invariant approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 1998, 20(6): 590–603.
[60] H. Li, B.S. Manjunath, S.K. Mitra. A contour-based approach to multisensor image registration. IEEE Transactions on Image Processing, Mar. 1995, 4(3): 320–334.
[61] J. le Moigne. Parallel registratin of multi-sensor remotely sensed imagery using wavelet coefficients. Proceedings of the SPIE: Wavelet Applications, Orlando, Florida, 2242, 1994: 432–443.
[62] Giancarlo Rufino, Antonio Moccia, and Salvatore Esposito. DEM Generation by Means of ERS Tandem Data. IEEE Transaction on Geosci. Remote Sensing, Jan 1998, 36(6): 1905-1912.
[63] Adrian Luckman and William Grey. Urban Building Height Variance From Multibaseline ERS Coherence. IEEE Transaction on Geosci. Remote Sensing, Jan. 2003, 41(9): 2022-2025,.
[64] R. Abdelfattah, J. M. Nicolas, and F. Tupin. Interferometric SAR image coregistartion based on the Fourier-Mellin Invariant descriptor. Geoscience and Remote Sensing Symposium, 2002. IGARSS '02, Jun. 2002, 3:1334- 1336.
[65] Harold S. Stone, Michael T. Orchard, Ee-Chien Chang, andStephen A. Martucci. A Fast Direct Fourier-Based Algorithm for Subpixel Registration of Images. IEEE Trans. Geosci. Remote Sensing, Oct. 2001, 39(10): 2235–2243.
[66] Harold S. Stone, and Robert Wolpov. Blind Cross-Spectral Image Registration Using Prefiltering and Fourier-Based Translation Detection. IEEE Trans. Geosci. Remote Sensing, Mar. 2002, 40(3): 637-649.
[67] R.N. Bracewell. The Fourier Transform and Its Applications. McGraw-Hill, New York, 1965.
[68] H. Foroosh, J.B. Zerubia, M. Berthod. Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing, Mar. 2002, 11(3): 188–200.
[69] P.E. Anuta. Spatial registration of multispectral and multitemporal digital imagery using Fast Fourier Transform. IEEE Transactions on Geoscience Electronics, 1970: 353–368.
[70] Jordi Inglada and Alain Giros. On the Possibility of Automatic Multisensor Image Registration. IEEE Trans. Geosci. Remote Sensing, Oct. 2004, 42(10): 2104–2120.
[71] Arlene A. Cole-Rhodes, Kisha L. Johnson, Jacqueline LeMoigne, and Ilya Zavorin. Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient. IEEE Trans. Image Processing, Jan. 1998, 12(12): 27–41.
[72] Hua Xie, Leland E. Pierce, and Fawwaz T. Ulaby. Mutual Information Based Registration of SAR Images. Geoscience and Remote Sensing Symposium, 2003. IGARSS '03, Jul. 2003, 6: 4028-4031.
[73] Hua-Mei Chen, Pramod K. Varshney, and Manoj K. Arora. Performance of Mutual Information Similarity Measure for Registration of Multitemporal Remote Sensing Images. IEEE Trans. Geosci. Remote Sensing, Nov. 2003, 41(11): 2445-2454.
[74] Farzin Mokhtarian and Riku Suomela. Robust Image Corner Detection Through Curvature Scale Space. IEEE Transactions on Pattern Analysis and Machine Intelligence, Dec. 1998, 20(12): 1376–1381.
[75] Zhengwei Yang and Fernand S. Cohen. Image Registration and Object Recognition Using Affine Invariants and Convex Hulls. IEEE Transactions on Image Processing, July. 1999, 8: 934-946.
[76] J. Canny. A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 8 (1986) 679–698.
[77] Klaus Arbter, Wesley E. Snyder, Hans Burkhardt. Application of affine-invariant fourier descriptors to recognition of 3-D objects. IEEE Transactions on Pattern Analysis and Machine Intelligence, Jul. 1990,12(7): 640-647.
[78] M. J. Paulik, M. Das, and N. K. Loh. Nonstationary autoregressive modeling of object contours. IEEE Trans. Signal Processing, 1992,40(3):660-675.
[79] F. S. Cohen, Z. Huang, and Z. Yang. Invariant matching and identification of curves using B-splines curve representation. IEEE Trans. Image Processing, Jan. 1995, 4(1): 1-10.
[80] Zhaohui Huang and Fernand S. Cohen. Affine-Invariant B-Spline moments for curve matching. IEEE Trans. Image Processing, Oct. 1996, 10(10):1473-1480.
[81] Minghui Xia,and Bede Liu. Image registration by “Super-Curves”. IEEE Trans. Image Processing, May. 2004, 13(5):720-732.
[82] 刘宝泉,冯大政,武楠,李军侠.基于点特征的干涉合成孔径雷达(InSAR) 复图像自动配准算法. 航空学报,2007, 28(1):161-166.
[83] 刘宝泉,冯大政,李军侠,武楠. 基于曲线拟合的干涉合成孔径雷达(InSAR) 复图像自动配准算法.电子与信息学报,2007, 29(7):1666-1669.
[84] 刘宝泉,冯大政,武楠,李军侠. 干涉合成孔径雷达复图像自动配准算法.西安电子科技大学学报,2006,33(6): 887-891.
[85] A.L. Gray, K.E. Mattar, and P.W. Vachon. InSAR Results from the RADARSAT Antarctic Mapping Mission Data: Estimation of Glacier Motion using a Simple Registration Procedure. Geoscience and Remote Sensing Symposium Proceedings, Jul. 1998, 3: 1638-1640.
[86] Q. Lin, J. F. Vesecky, and H. A. Zebker. Registration of interferometric SAR images. in Proc. IGARSS, Houston, TX, May. 1992: 1579–1581.
[87] P. Stoica, Y. Selen. Cyclic minimizers, majorization techniques, and the expectation maximization algorithm: a refresher. IEEE Signal Processing Magazine, Jan. 2004, 21(1): 112-114.
[88] J. H. Mathews, K. D. Fink. Numerical Methods Using MATLAB. Third Edition, Publishing House of Electronics Industry.
[89] Epsilon Nought, Radar Remote Sensing. Sample data. [Online]. Available: http://epsilon.nought.de/.
[90] Trouvé E., Nicolas J. M., and Ma?tre H. Improving phase unwrapping techniques by the use of local frequency estimates. IEEE Trans. Geosci. Remote Sensing, November. 1998, 36(6): 1963-1972.
[91] Goldstein R. M. and Werner C. L. Radar interferogram filtering for geophysical applications. Geophys. Res. Lett., Mar. 1998, 25(21): 4035–4038.
[92] López-Martínez C., and Fàbregas X. Modeling and Reduction of SAR Interferometric Phase Noise in the Wavelet Domain. IEEE Trans. Geosci. Remote Sensing, Dec. 2002, 40(12): 2553–2566.
[93] Suksmono A. B., and Hirose A. Adaptive Noise Reduction of InSAR Images Based a Complex-Valued MRF Model and Its Application to Phase Unwrapping Problem. IEEE Trans. Geosci. Remote Sensing, March. 2002, 40(3): 699–709.
[94] Lorenzo-Ginori J. V., Plataniotis K. N., and Venetsanopoulos A. N. Nonlinear filtering for phase image denoising. IEE Proc-Vis. Image Signal Process., Oct. 2002, 149(5): 290-296.
[95] Wu N, Feng D. Z, and Li J. X. A Locally Adaptive Filter of Interferometric Phase Images. IEEE Trans. Geosci. Remote Sensing Letters, Jan. 2006, 3(1): 73–77.
[96] Spagnolini U. 2-D phase unwrapping and instantaneous frequency estimation. IEEE Trans. Geosci. Remote Sensing, Mar. 1995, 33(3): 579-589.
[97] Fornaro G., Franceschetti G., and Lanari R. Interferometric SAR Phase Unwrapping Using Green's Formulation. IEEE Trans. on Geosci. Remote Sensing, May. 1996, 34(3): 720-728.
[98] Fornaro G., Franceschetti G. Robust phase unwrapping techniques: A Comparison. J. Opt. Soc. Am. A, Dec. 1996, 13(12): 2355-2366.
[99] Ghiglia D. C., and Romero L. A. Minimum LP-norm two-dimensional phase unwrapping. J. Opt. Soc. Amer A, Oct. 1996, 13(10): 1999-2013.
[100] Fornaro G., Sansosti E. A two-dimensional region growing least squares phase unwrapping algorithm for interferometric SAR processing. IEEE Trans. on Geosci. Remote Sensing, Sep. 1999, 37(5): 2215-2226.
[101] Xu W., and Cumming I. A region growing algorithm for InSAR phase unwrapping. IEEE Trans. on Geosci. Remote Sensing, Jan. 1999, 37(1): 124-134.
[102] Chen C. W., and Zebker H. A. Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms. J. Opt. Soc. Amer A, 2000, 17(3): 401-414.
[103] Carballo G. F., and Fieguth P. W. Probabilistic Cost Functions for Network Flow Phase Unwrapping. IEEE Trans. on Geosci. Remote Sensing, Sep. 2000, 38(5): 2192-2201.
[104] Chen, C.W., and Zebker, H.A. Two-dimensional phase unwrapping with use of statistical models for cost functions in nonlinear optimization. Journal of the Optical Society of America A, Feb. 2001, 18(2): 338-51.
[105] Trouv′e E., Caramma M., and Ma?ytre H. Fringe detection in noisy complex interferograms. Appl. Opt., July. 1996, 35(20): 3799–3806.
[106] 李笑郁、毛士艺. 分支阻断干涉 SAR 相位展开算法的解析与实现. 电子学报,2001 年 12 月,12(z1): 1790-1793.
[107] Pritt, M. D. Phase unwrapping by means of multigrid techniques forinterferometric SAR. IEEE Trans. on Geosci. Remote Sensing, May. 1996, 34(3): 728-738.
[108] 武楠,冯大政,刘宝泉.一种基于枝切法和有限元法的干涉SAR合成相位展开方法. 电子与信息学报,2007, 29(4): 846-850.
[109] Ghiglia D. C., Pritt M. D., Two-Dimensional Phase Unwrapping: Theory, Algorithms, and Software. New York: Wiley, 1998: 74-76.
[110] Kwon Y. W., Bang H., The Finite Element Method Using MATLAB, New York: CRC, 2000: 85-86.
[111] 张贤达著. 现代信号处理. 清华大学出版社,第二版,2002年.
[112] Giovanni Corsini, Marco Diani, Fabrizio Lombardini, and Gianpaolo Pinelli. Simulated Analysis and Optimization of a Three-Antenna Airborne InSARSystem for Topographic Mapping. IEEE Trans on Geosci and Remote Sensing, Sep. 1999, 37(5): 2518-2529.
[113] Lombardini F. Absolute Phase Retrieval in a Three-element Synthetic Aperture Radar Interferometer. Proc.1996 CIE International Conference of Radar, Beijing, China, 8th-10th October. 1996: 309?312.
[114] Ghiglia D C, Wahl D E. Interferometric Synthetic Aperture Radar Terrain Elevation Mapping from Multiple Observations. IEEE Digital Signal Processing Workshop, 1994: 33?36.
[115] F. Lombardini, F. Gini, and P. Matteucci. Application of Array Processing Techniques to Multibaseline InSAR for Layover Solution. Radar Conference, 2001: 210 – 215.
[116] Fabrizio Lombardini and Fulvio Gini. Multiple Reflectivities Estimation for Multibaseline InSAR Imaging of Layover Extended Sources. Radar Conference, Proceedings of the International, 2003: 257 – 263.
[117] Pierfrancesco Lombardo, Fabrizio Lombardini. Multi-baseline SAR interferometry for Terrain Slope Adaptivity. Radar Conference, IEEE National 1997:196 – 201.
[118] Lei Ying, David . Munson, RalfKoetter, Brendan J. Multibaseline InSAR Terrain Elevation Estimation: A Dynamic Programming Approach. Image Processing, Proceedings, 2003: 157-160.
[119] J. J. van Zyl and R. Chellappa. Bayesian classification of polarimetric SAR images using adaptive a priori probabilities. Int. J. Remote sens. 1992:835-840.
[120] S. R. Cloude and E. Pottier. A review of target decomposition theorems in radar polarimetry. IEEE Trans on Geosci and Remote Sensing, 1996, 34: 498-518.
[121] D. L. Schuler, J. S. Lee, and G. De Grandi. Measurement of topography using polarimetric SAR images. IEEE Trans on Geosci and Remote Sensing, 1996, 34: 1266-1277.
[122] S. R. Cloude, and K. P. Papathanassiou. polarmetric SAR interferometry. IEEE Trans on Geosci and Remote Sensing, Sep. 1998. 36(5): 1551-1565.
[123] K. P. Papathanassiou and A. Reigber. On the interferometric coherence: A multifrequency and multitemporal analysis. In Proc. FRINGE’96 workshop. ESA, Zurich, Switzerland, 1996: 319-330.
[124] R. N. Treuhaft, S. N. Madsen, and M. Moghaddam. Vegetation characteristics and underlying topography from interferometric data. Radio Sci, 1996:1449-1495.
[125] J. O. Hagberg, L. M. Ulander, and J. Askne. Repeat-pass SAR interferometry over forested terrain. IEEE Trans on Geosci and Remote Sensing, Mar. 1995, 33(2): 331-340.
[126] J. Askne, P. B. Dammert, L. M. Ulander, and G. Smith. C-band repeat-pass interferometric SAR observations of the forest. IEEE Trans on Geosci and Remote Sensing, Jan 1997, 35(1): 331-340.