星载SAR及InSAR技术在地球科学中的应用研究
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摘要
星载SAR(Synthetic Aperture Radar，合成孔径雷达)及InSAR技术(Interferometric Synthetic Aperture Radar，合成孔径雷达干涉测量)是近三十年来发展起来的空间对地观测新技术，是未来三维测图与区域地形形变监测领域最具潜力的新技术之一，在农林业、海洋、地形及形变测绘、地质、生态等地球科学领域发挥着日益重要的作用。考虑到我国该领域的研究起步较晚，远远落后美国、欧洲等发达国家。2006年，我国第一颗SAR小卫星“遥感卫星一号”发射成功，更促使我国加速开展这方面的研究，以尽快赶上国际先进水平。
在这样的背景下，本文从星载SAR原始数据(Level 0)出发，阐述了星载SAR成像的机理以及成像质量评定指标，提出了InSAR对成像算法及成像产品的几点要求，紧接着系统、全面地概括了(D-)InSAR技术的基本原理及局限性。在这些理论的支撑下，本文围绕星载SAR成像以及星载SAR、InSAR技术的应用主要做了以下工作：
(1)星载SAR成像处理。成像处理是SAR技术的核心部分。第三章分析了星载SAR点目标回波信号模型以及几种常用的数字成像处理算法，接着介绍了距离—多普勒算法的数据处理流程及几个关键的问题；然后在第五章采用ERS-1、Radarsat-1以及JERS-1卫星数据利用EV-APP软件做了成像实验，并对成像质量做了评价。
(2)星载SAR在海洋气象学中的应用—星载SAR图像反演海面风矢量研究。首先介绍了星载SAR估算海面风场的基本原理、算法及数据处理步骤。接着，以2002年5月7日香港地区ERS—2 SAR海洋图像为例，对经典的SWDA(SAR Wind Direction Algorithm)—谱分析方法加以改进，求得具有180°模糊度的风向，并用香港天文台气象浮标实测数据消除了风向不确定性。最后，利用CMOD4 GMF(Geophysical Model Function，地球物理模式函数)计算得到海面上10m高的风速。与浮标实测资料相比，文中两个20km×20km试验区域ROI1与ROI2风向误差分别为23.71°、7.00°，平均风速误差分别为0.18m／s、—0.12m／s，精度均较高。由此可以得出结论：如果对SAR预先进行ADC(Analog Digital Converter)改正以及精确校准，结合改进的SWDA和CMOD4，可以获得高精度的风矢量。(3)二轨差分干涉(D-InSAR)技术监测抚顺市矿山沉降。文中利用二轨差分干涉测量技术，使用ERS-1／ERS-2卫星1993年5月22日至2000年6月13日所获取的辽宁省抚顺地区6幅SAR图像以及SRTM 3×3弧秒分辨率的DEM，研究了抚顺市的沉降情况，监测精度达cm级。需要指出的是，文中采取了一种全新的干涉相位图频域滤波方法，使用这种算法使得差分干涉图的质量平均改善了28％。实验证明，D-InSAR技术可以有效地监测矿区沉降情况。
论文的创新性或特点表现在以下几个方面：(1)从公开的文献看，本文在国内首次同时对不同卫星的SAR原始数据进行成像试验，并对成像的结果进行了定量评价，为InSAR数据处理中某些问题的解决提供了参考思路；(2)其次，星载SAR在海洋学、气象学方面的应用研究是极具挑战性和发展潜力的课题，本文从星载SAR图像提取了香港地区的风向和风速，并对反演算法进行了改进，从而为海洋及气象服务提供更加可靠的资料，这是具有前瞻性的研究，；(3)我国矿区InSAR沉降监测可应用性研究并不多见。本文利用D-InSAR这种新兴的技术对国内普遍存在的矿山沉降问题进行深入研究，并在数据处理中将新的频域滤波算法用于干涉相位图，取得了相当好的结果。
Space borne SAR (Synthetic Aperture Radar) and InSAR (Interferometric SAR)are two kinds of new modern remote sensing space geodesy technologies, which havegot rapid development during the last 30 years. The two technologies providebrand-new tools for the 3D topographic mapping and earth surface deformationmonitoring, and play more and more important roles in earth science, such asagriculture and forestry, oceanology, topography and deformation surveying, geology,and ecology etc. Compared with America and European developed countries, studieson SAR and InSAR in China have relatively fallen behind. Until 2006, Chinese firstsmall SAR satellite was launched successfully, which spurred us to carry out morerelated researches as soon as possible so as to shorten the distance between China anddeveloped countries.
Under this background, this paper introduces the principles of space borne SARimaging processing and several indexes which can reflect imaging quality. And thenilluminates the principles and limitations of InSAR and D-InSAR techniques.Subsequently, this paper focuses on the SAR imaging and the applications of SARand InSAR, and carries out some researches summarized as follows:
(1) Space-borne SAR imaging processing. Imaging processing is the kernel of SARtechnique. Chapter 3 analyzes the echo signal model of point target; introducesseveral SAR imaging algorithms, illuminates the essential of range compressing andazimuth compressing during the Range-Doppler algorithm, discusses some keyproblems, such as Doppler parameters estimation, range migration correction andspeckle noise elimination in detail; and then adopts SAR raw data of Radarsat-1,ERS-1, and JERS-1 to do some imaging tests in chapter 5. Finally, we getencouraging imaging results.
(2) The application of Space-borne SAR on the marine meteorology-sea windinversion by SAR image, seen in chapter 6. We first introduce the principle and threemain algorithms of ocean wind retrieval with SAR images. Then we provide the flowchart of retrieval procedures. As an example, ERS-2 SAR images covering HongKong region acquired on May 7, 2002 are used to carry out test of wind vectorretrieval. The data processing includes pre-processing of SAR image, ADC (AnalogDigital Converter) compensation, accurate calibration and speckle removal, and theapplication of classical SAR Wind Direction Algorithm for wind direction retrieval.Data collected on a buoy by the Hong Kong Observatory are then used to resolve thewind direction uncertainty. Finally, the GMF (Geophysical Model Function)-CMOD4is adopted to estimate the wind speed at the height of 10m above sea level. Comparedwith the data recorded by Hong Kong Observatory, the errors in the wind direction inthe areas of ROI1 and ROI2 are 23.71°and 7.00°, respectively, and the errors inaverage wind speed are 0.18 m/s and—0.12 m/s. The results show that betterpre-processing, together with the use of classical spectral analysis algorithm andCMOD4 model can offer high quality wind vector results.
(3) Monitoring mining subsidence with two-pass D-InSAR technique in Fushun city,China. We use 6 ERS-1 and ERS-2 SAR images acquired from May 22, 1993 to Jun13, 2000 and covering whole Fushun city, and the SRTM DEM with3 arc-secondresolution to conduct two-pass differential SAR interferometry. They can form 15differential interferograms in total, but only 5 with satisfactory baseline and coherence.By investigating the 5 interferometric pairs, we obtain the subsidence rate of Fushuncity. During the data processing, we use a new modified Goldstein filtering algorithmin frequency field to filter the differential and greatly improved the interferogramquality. Finally, the case study verifies that D-InSAR technique is able to detectcm-level subsidence of the mining area.
Some points of the former works deserve to be mentioned: (1) First, this paperprocesses the raw data of several satellites received by different satellite stations andcarry out related imaging tests, which provides possible answer to the uncertainproblems in InSAR data processing. (2) Second, as predicted the applications ofspace-bome SAR on the oceanology and meteorology will be one of the mostchallenging and valuable projects in the near future, meanwhile similar works inChina have not got enough attention relatively. This paper estimates the sea windvector using space-borne SAR image, and have done little improvement for theinversion algorithm, thus offered more reliable data for meteorology services. (3)Studies on mining subsidence using D-InSAR are very rare in the field of earth sciences.Therefore this paper adopts new technique-D-InSAR to investigate the miningsubsidence, and uses a brand-new modified Goldstein filtering algorithm in frequencyfield to filter the interferogram phase maps and get very good results.
在这样的背景下，本文从星载SAR原始数据(Level 0)出发，阐述了星载SAR成像的机理以及成像质量评定指标，提出了InSAR对成像算法及成像产品的几点要求，紧接着系统、全面地概括了(D-)InSAR技术的基本原理及局限性。在这些理论的支撑下，本文围绕星载SAR成像以及星载SAR、InSAR技术的应用主要做了以下工作：
(1)星载SAR成像处理。成像处理是SAR技术的核心部分。第三章分析了星载SAR点目标回波信号模型以及几种常用的数字成像处理算法，接着介绍了距离—多普勒算法的数据处理流程及几个关键的问题；然后在第五章采用ERS-1、Radarsat-1以及JERS-1卫星数据利用EV-APP软件做了成像实验，并对成像质量做了评价。
(2)星载SAR在海洋气象学中的应用—星载SAR图像反演海面风矢量研究。首先介绍了星载SAR估算海面风场的基本原理、算法及数据处理步骤。接着，以2002年5月7日香港地区ERS—2 SAR海洋图像为例，对经典的SWDA(SAR Wind Direction Algorithm)—谱分析方法加以改进，求得具有180°模糊度的风向，并用香港天文台气象浮标实测数据消除了风向不确定性。最后，利用CMOD4 GMF(Geophysical Model Function，地球物理模式函数)计算得到海面上10m高的风速。与浮标实测资料相比，文中两个20km×20km试验区域ROI1与ROI2风向误差分别为23.71°、7.00°，平均风速误差分别为0.18m／s、—0.12m／s，精度均较高。由此可以得出结论：如果对SAR预先进行ADC(Analog Digital Converter)改正以及精确校准，结合改进的SWDA和CMOD4，可以获得高精度的风矢量。(3)二轨差分干涉(D-InSAR)技术监测抚顺市矿山沉降。文中利用二轨差分干涉测量技术，使用ERS-1／ERS-2卫星1993年5月22日至2000年6月13日所获取的辽宁省抚顺地区6幅SAR图像以及SRTM 3×3弧秒分辨率的DEM，研究了抚顺市的沉降情况，监测精度达cm级。需要指出的是，文中采取了一种全新的干涉相位图频域滤波方法，使用这种算法使得差分干涉图的质量平均改善了28％。实验证明，D-InSAR技术可以有效地监测矿区沉降情况。
论文的创新性或特点表现在以下几个方面：(1)从公开的文献看，本文在国内首次同时对不同卫星的SAR原始数据进行成像试验，并对成像的结果进行了定量评价，为InSAR数据处理中某些问题的解决提供了参考思路；(2)其次，星载SAR在海洋学、气象学方面的应用研究是极具挑战性和发展潜力的课题，本文从星载SAR图像提取了香港地区的风向和风速，并对反演算法进行了改进，从而为海洋及气象服务提供更加可靠的资料，这是具有前瞻性的研究，；(3)我国矿区InSAR沉降监测可应用性研究并不多见。本文利用D-InSAR这种新兴的技术对国内普遍存在的矿山沉降问题进行深入研究，并在数据处理中将新的频域滤波算法用于干涉相位图，取得了相当好的结果。
Space borne SAR (Synthetic Aperture Radar) and InSAR (Interferometric SAR)are two kinds of new modern remote sensing space geodesy technologies, which havegot rapid development during the last 30 years. The two technologies providebrand-new tools for the 3D topographic mapping and earth surface deformationmonitoring, and play more and more important roles in earth science, such asagriculture and forestry, oceanology, topography and deformation surveying, geology,and ecology etc. Compared with America and European developed countries, studieson SAR and InSAR in China have relatively fallen behind. Until 2006, Chinese firstsmall SAR satellite was launched successfully, which spurred us to carry out morerelated researches as soon as possible so as to shorten the distance between China anddeveloped countries.
Under this background, this paper introduces the principles of space borne SARimaging processing and several indexes which can reflect imaging quality. And thenilluminates the principles and limitations of InSAR and D-InSAR techniques.Subsequently, this paper focuses on the SAR imaging and the applications of SARand InSAR, and carries out some researches summarized as follows:
(1) Space-borne SAR imaging processing. Imaging processing is the kernel of SARtechnique. Chapter 3 analyzes the echo signal model of point target; introducesseveral SAR imaging algorithms, illuminates the essential of range compressing andazimuth compressing during the Range-Doppler algorithm, discusses some keyproblems, such as Doppler parameters estimation, range migration correction andspeckle noise elimination in detail; and then adopts SAR raw data of Radarsat-1,ERS-1, and JERS-1 to do some imaging tests in chapter 5. Finally, we getencouraging imaging results.
(2) The application of Space-borne SAR on the marine meteorology-sea windinversion by SAR image, seen in chapter 6. We first introduce the principle and threemain algorithms of ocean wind retrieval with SAR images. Then we provide the flowchart of retrieval procedures. As an example, ERS-2 SAR images covering HongKong region acquired on May 7, 2002 are used to carry out test of wind vectorretrieval. The data processing includes pre-processing of SAR image, ADC (AnalogDigital Converter) compensation, accurate calibration and speckle removal, and theapplication of classical SAR Wind Direction Algorithm for wind direction retrieval.Data collected on a buoy by the Hong Kong Observatory are then used to resolve thewind direction uncertainty. Finally, the GMF (Geophysical Model Function)-CMOD4is adopted to estimate the wind speed at the height of 10m above sea level. Comparedwith the data recorded by Hong Kong Observatory, the errors in the wind direction inthe areas of ROI1 and ROI2 are 23.71°and 7.00°, respectively, and the errors inaverage wind speed are 0.18 m/s and—0.12 m/s. The results show that betterpre-processing, together with the use of classical spectral analysis algorithm andCMOD4 model can offer high quality wind vector results.
(3) Monitoring mining subsidence with two-pass D-InSAR technique in Fushun city,China. We use 6 ERS-1 and ERS-2 SAR images acquired from May 22, 1993 to Jun13, 2000 and covering whole Fushun city, and the SRTM DEM with3 arc-secondresolution to conduct two-pass differential SAR interferometry. They can form 15differential interferograms in total, but only 5 with satisfactory baseline and coherence.By investigating the 5 interferometric pairs, we obtain the subsidence rate of Fushuncity. During the data processing, we use a new modified Goldstein filtering algorithmin frequency field to filter the differential and greatly improved the interferogramquality. Finally, the case study verifies that D-InSAR technique is able to detectcm-level subsidence of the mining area.
Some points of the former works deserve to be mentioned: (1) First, this paperprocesses the raw data of several satellites received by different satellite stations andcarry out related imaging tests, which provides possible answer to the uncertainproblems in InSAR data processing. (2) Second, as predicted the applications ofspace-bome SAR on the oceanology and meteorology will be one of the mostchallenging and valuable projects in the near future, meanwhile similar works inChina have not got enough attention relatively. This paper estimates the sea windvector using space-borne SAR image, and have done little improvement for theinversion algorithm, thus offered more reliable data for meteorology services. (3)Studies on mining subsidence using D-InSAR are very rare in the field of earth sciences.Therefore this paper adopts new technique-D-InSAR to investigate the miningsubsidence, and uses a brand-new modified Goldstein filtering algorithm in frequencyfield to filter the interferogram phase maps and get very good results.
引文
[1] Bock Y, Williams S. Integrated satellite interferometry in southern California[J]. EOS Transactions of the American Geophysical Union, 1997, 78(29): 293, 299-300.
[2] Boerner W M, Cloude S R, Lee J S, et al. Advances in Extra Wide-Band Multi-modal Air/Space-borne Radar Polarimetry, POL-IN-SAR Imaging and its Applications [J]. IEEE Transactions on Geoscience and Remote Sensing, 2002:408-410.
[3] Bonforte A, Ferretti A, Prati C, et al. Calibration of atmospheric effects on SAR interferograms by GPS and local atmosphere models: first results [J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2001, 63:1343-1357.
[4] Brandfass M. Generation of Bald Earth Digital Elevation Models as Applied to Polarimetric SAR interferometry [A]. In: IGARSS'02. IEEE [C]. Toronto, Canada, 2002.2:1014-1016.
[5] Carnec C, Delacourt C. Three years of mining subsidence monitored by SAR interferometry, near Gardanne, France[J]. Journal of Geophysics. 2000. 43:43-54
[6] Cloude S R, Papathanassiou K P. Polarimetric SAR interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing. 1998, 36(5): 1551-1565.
[7] Cloude S R. Robust Parameters Estimation using Dual Baseline Polarimetric SAR Interferometry [A], In:IGRASS'02, IEEE [C]. Toronto. Canada. 2002, 2:838-840.
[8] Colesanti C. Ferretti A, Prati C, et al. Monitoring landslides and tectonic motions with the Permanent Scatterers Technique [J]. Engineering Geology, 2003, 68: 3-14.
[9] Colesanti C, Ferretti A, Prati C, et al. Multi-image satellite SAR interferometry: state of the art and future trends [A]. In: Proceedings of the International Radar Conference. IEEE [C]. Toulouse, France. 2003:239-244.
[10] Crosseto M, Tscherning C, Crippa B, et al. Subsidence Monitoring using SAR interferometry: Reduction of the atmospheric effects using stochastic filtering [J]. Geophysical Reaearch Letters, 2002, 29(9). 26-29.
[11] Ding. X. L., Li, Z. W., Huang, C., and Zhu, J. J., Selection of Filtering Parameter for the Goldstein Radar Interferogram Filter[J](submitted). IEEE Transactions on Geoscience and Remote Sensing. 2006.
[12] Eineder M, Adam N. Avoiding phase unwrapping in DEM generation by fusing multi frequency ascending and descending interferograms [A]. In: Proceedings of IGARS'04, IEEE[C]. Alaska, USA, 2004: 477-480.
[13] Ferretti A, Guarnieri A M, Prati C, et al. Multibaseline Interferometric Techniques and Applications [A]. In: Fringe 96' Workshop on ERS SAR interferometry[C]. Zurich, Switzerland, 1997: 17-21.
[14] Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry. In: Proceedings of IGARSS'99, IEEE [C]. Hamburg, Germany, 1999, 3: 1528-1530.
[15] Ferretti A, Prati C, Rocca F.Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry [J]. IEEE Transactions on GeoScience and Remote Sensing, 2000, 38(5): 2202-2212.
[16] Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20.2004, 29 (8): 664-668.
[17] Fetterer F, Gineris D, Wackerman C. Validating a scatterometer wind algorithm for ERS-1 SAR[J]. IEEE Trans. On Geosciences and Remote Sensing, 1998, 36(2): 479-492.
[18] Gabriel A K, Goldstein R M, Zebker H A. Mapping small elevation changes over large areas: Differential radar interferometry [J]. Journal of Geophysical Research, 1989, 94(B7): 9183-9191.
[19] Gatellif F., Guarnieri A M, Parizzi F, Pasquali P, Prati C, Rocca F. The wavenumber shift in SAR interferometry, IEEE transactions on Geosciences and Remote Sensing, 1994, 32(4): 855-865.
[20] Ge L L, Chang H C, Rizos C, Omura M. Mine Subsidence Monitoring: a Comparison among ENVISAT, ERS and JERS-1, Proceedings of 2004 ENVISAT Symposium[C], Salzburg, Austria, 6-10 September, Session 3: 4-9.
[21] Ge L L, Rizos C, Han S W, Zebker H. Mining Subsidence Monitoring Using the Combined InSAR and GPS Approach [C]. The 10th FIG international symposium on deformation measurements, California: Orange, 2001.
[22] Ghiglia D C, Wahl D E. Interferometric synthetic aperture radar terrain elevation mapping from multiple observations [A]. In: Proceedings of the 1994 Sixth IEEE Digital Signal Processing Workshop [C]. Yoseminte National Park, Canada, 1994: 33-36.
[23] Goldstein R M, Zebker H A. Interferometric radar measurement of ocean surface currents [J]. Nature, 1987, 328: 707-709.
[24] Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: Two dimensional phase unwrapping [J]. Radio Science, 1988, 23(4): 713-720.
[25] Goldstein R. M. and Werner, C. L., Radar Interferogram Filtering for Geophysical Applications [J]. Geophysical Research Letter, 1998, 25(21): 4035-4038.
[26] Goldstein R. Atmospheric limitations to repeat-track radar interferometry [J]. Geophysical Research Letter, 1995, 22(18): 2517-2520.
[27] Graham L C, Synthetic Interferometer Radar for Topographic Mapping[J]. Proceedings of IEEE, 1974, 62(6): 763-768.
[28] Hajnsek I, Cloude S R. Pol-InSAR for agricultural vegetation parameter estimation[A]. In: Proceedings of IGARSS'04, IEEE [C]. Anchorage, USA, 2004, 2: 1224-1227.
[29] Hanssen R.F, Wechwerth T M, Zebker H A, et al. High-resolution water vapor mapping from interferometric radar measurements [J]. Science, 1999, 283: 1297-1299.
[30] Harding D J, Blair J B, Rodriguez E, et al. Airborne laser altimetry and interferometric SAR measurements of canopy structure and sub- canopy topography in the Pacific Northwest [A]. In: Proceedings of Second Symposium on Combined Optical-Microwave Earth and Atmosphere Sensing(COMEAS) [C]. Piscataway, New Jersey, USA, 1995: 22-24.
[31] Hassen R. Atmospheric heterogeneities in ERS tandem SAR interferometry [M]. Delft: Delft University Press, 1998.
[32] Hanssen R F. Radar Interferometry: Data Interpretation and Error Analysis[M]. Kluwer Academic Publishers, Dordercht, The Netherlands, 2001.
[33] Hersbach H. CMOD5—An improved geophysical model function for ERS C-band scatterometry. European Center for Medium Range Weather Forecasts, internal report[EB/OL], 2003.
[34] Homer J, Longstaff I D, Callaghan G. High resolution 3-D SAR via multi, baseline interferometry [A]. In: Proceedings of IGARSS'96, IEEE [C]. Lincoln, Nebraska, USA, 1996: 796-798.
[35] Horstmann J, Koch W, Lehner S, et al. Wind Retrieval Over The Ocean Using Synthetic Aperture radar with C-band HH Polarization [J]. IEEE Trans. On Geosciences and Remote Sensing, 2000, 38(5): 2122-2131.
[36] Horstmann J, Schiller H, Schulz-Stellenfleth J, et al. Global wind speed retrieval from SAR[J]. IEEE Trans. On Geosciences and Remote sensing, 2003, 41 (10): 2277-2286.
[37] InSAR Project Description of Caltech's Center for Advanced Computing Research [EB/OL]. http://www.cacr.caltech.edu/SDA/InSAR/insardesc.html.
[38] Jarosz A, Wanke D. Use of InSAR for Monitoring of Mining Deformations[C]. Proc. of FRINGE 2003 Workshop, Frascati. Italy, 1-5 December 2003, ESA SP-550, Jun 2004.
[39] Johannessen J A, Vachon P W, Johannessen O M. ERS-1 SAR imaging of marine boundary layer processes[C]. ESA, Earth Observation Quarterly. Paris, France, 1994a, 46: 1-256.
[40] Jong-sen Lee, Cloude S R, Papathanassiou K P, et al. Speckle filtering and coherence estimation of polarimetric SAR interferometry data for forest applications [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41 (10): 2254-2263.
[41] Just D. Bamler Phase statistics of interfergrams with applications to synthetic aperture rasar[J], Appl. Optics, 1994, 33(20): 4361-4368.
[42] Kerbaol V, Chapron B, Vachon P.W. Analysis of ERS-1/2 Synthetic Aperture Radar Wave Mode Imagettes [J]. Journal of Geophysical Research, 1998, 103(C4): 7833-7846.
[43] Kim Y J, Zyl J V. Overview of Polarimetric Interferometry [A]. In: Proceedings of IEEE 2000 Aerospace Conference [C]. Big Sky, Montana, USA, 2000, 3: 231-236.
[44] Korsbakken E, Johannessen J A, Johannessen O M. Coastal Wind Field Retrievals from ERS Synthetic Aperture Radar Imagesp[J]. Journal of Geophysical Research, 1998, 103(C4): 7857-7874.
[45] Li T, Liu J N, Liao M S, Kuang S J, Lu X. Monitoring city subsidence by D-InSAR in Tianjin area [A]. In: Proceedings of IGARSS'04, IEEE [C]. Anchorage, USA, 2004, 5: 3333-3336.
[46] Li X W, Guo H D, Wang C L, et al. Generation and Error Analysis of DEM Using Spaceborne Polarimetric SAR Interferometry Data [A]. In: IGARSS'02, IEEE [C]. Toronto, Canada, 2002, 5: 2705-2707.
[47] Li Z L, Zou W B, Ding X. L., Chen. Y Q, and Liu G X. A Quantitative Measure for the Quality of InSAR Interferograms Based on Phase Differences[J]. Photogrammetric Engineering & Remote Sensing, 2004, 70(10): 1131-1137.
[48] Li Z W, Ding X L, Liu G X, et al. Atmospheric effects on InSAR measurements-a review [J]. Geomatics Research Australasia, 2003c, 79: 43-58.
[49] Li Z W, Ding X L, Liu G X. Modeling atmospheric effects on InSAR with meteorological and continuous GPS observations: algorithms and some test results [J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2004a, 66: 907-917.
[50] Living on a restless planet. Solid Earth science working group report 2002 [EB/OL]. http://solidearth.jpl.nasa.gov.
[51] Luciano V D, Marcos T E, Britaldo S S, et al. Assessment of Digital Elevation Models Obtained in Brazilian Amazon Based on P and X band airborne Interferometric Data [A]. In: IGARSS'02, IEEE [C]. Toronto, Canada, 2002: 3617-3619.
[52] Massonnet D, Rossi M, Carmona C, et al. The displacement field of the Landers earthquake mapped by radar interferometry [J]. Nature, 1993, 364(8): 138-142.
[53] Massonnet D, Feigl K L. Discrimination of geophysical phenomena in satellite radar interferograms [J]. Geophysical Research Letters, 1995, 22(12): 1537-1540.
[54] Nagler T, Rott H, Kamelger A. Analysis of landslides in Alpine areas by means of SAR interferometry [A]. In: Proceedings of IGARSS'02, IEEE [C]. Toronto, Canada, 2002, 1: 198-200.
[55] Nico G, Pappalepore M, Pasquariello G, et al. Comparison of ERS-1 SAR Data VS. coherence for flood detection methods-A GIS application [J]. International. Journal of Remote Sensing, 2000, 21 (8): 1619-1631.
[56] Nutricato R, Bovenga F, Refice A. Optimum interpolation and resampling for PSC identification [A]. In: Proceedings of IGARSS'02, IEEE [C]. Toronto, Canada, 2002, 6: 3626-3628.
[57] Papathanassiou K P, Moreira J R. Interferometric analysis of multifrequency and multipolarization SAR data [A]. In: Proceedings of IGARSS'96, IEEE[C]. Lincoln, Nebraska, USA, 1996: 1227-1229.
[58] Papathanassiou K P, Cloude S R. Polarimetric effects in repeat-pass SAR interferometry [A]. In: IGARSS'97, IEEE [C]. Singapore, 1997, 4: 1926-1928.
[59] Papathanassiou K P, Cloude S R. Single-Baseline Polarimetric SAR Interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2352-2363.
[60] Perski, Z., and Jura, D., ERS SAR Interferometry for Land Subsidence Detection in Coal Mining Areas [J]. EOQ 2001, 63: 21-25.
[61] Portabella M, Stoffelen A, Johannessen J A. Toward an Optimal Inversion Method for Synthetic Aperture Radar Wind Retrieval [J]. Journal of Geophysical Research, 2002.. 107(C8): 1-13.
[62] Rosen P A, Hensley S, Joughin I R, et al. Synthetic aperture radar interferometry [J]. Proceedings of IEEE, 2000, 88(3): 333-382.
[63] Shum C. K. Distributed Computing to Support Interdisciplinary Research in Space Geodesy and Earth Sciences Using the OSC 16-node SGI Cluster [EB/OL]. http://www.osc.edu/hpc/cluster_ohio/sites/rev2/osus.index.shtml.
[64] Slatton K C, Evans B L. Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2470-2482.
[65] Stoffelen A, Anderson D L T. Scatterometer data interpretation: Estimation and validation of the transfer function CM0D4 [J]. Journal of Geophysical Research, 1997,102(C3): 5767-5780.
[66] Tabb M, Flynn T, Carande R. An Extended Model for Characterizing Vegetation Canopies Using Polarimetric SAR Interferometry [A]. In: IGARSS'02, IEEE [C], Toronto, Canada, 2002:1020-1022.
[67] Thompson D R, Beal R C. Mapping High-Resolution Wind Fields Using Synthetic Aperture Radar[J]. J. Hopkins APL Tech. Digest, 2000, 21 (1):58-67
[68] Timmen L, Ye X, Reigber C. Monitoring of Small Motions in Mining Areas by SAR Interferometry [EB/OL]. http://earth.esa.int/workshops/fringe_1996/timmen/.
[69] Vachon P W, Dobson F W. Validation of Wind Vector Retrieval from ERS-1 SAR Images over the Ocean [J]. Global Atmos. Ocean Syst, 1996, 5(2): 177-187
[70] Wackermann C C, Rufenach C L, Shuchman R A, et al. Wind Vector Retrieval Using ERS-1 Synthetic Aperture Radar Imagery[J]. IEEE Trans. On Geosciences and Remote Sensing, 1996, 34(6): 1343-1352.
[74] Williams S, Bock Y, Fang P. Integrated satellite interferometry: tropospheric noise, GPS estimates and implications for interferometric synthetic aperture radar product [J]. Journal of Geophysical Research, 1998,103(B11): 27051-27067.
[72] Webley P W, Bingley R M, Dodson A H, et al. Atmospheric water vapor correction to InSAR surface motion measurements on mountains: results from a dense GPS network on Mount Etna [J]. Physics and Chemistry of the Earth, 2002, 27:363-370.
[73] Yijun He, William Perrie, Qingping Zou, et al. A New Wind Vector Algorithm for C-band SAR [J]. IEEE Trans. On Geosciences and Remote sensing. 2005, 43(7): 1453-1458.
[74] Zebker H A, Rosen P A, Hensley S. Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps [J]. Journal of Geophysical Research, 1997, 102(B4): 7547-7563.
[75] Zebker H.A, Villasenor J, Decorrelation in Interferometric Radar Echoes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30: 950-959.
[76] 蔡希尧．雷达系统概论[M]，科学出版社：1983．
[77] 李德仁，廖明生，王艳．永久散射体雷达干涉测量技术[J]．武汉大学学报(信息科学版)，2004，29(8)：664-668．
[78] 李德仁，杨杰．从卫星雷达提取地面高程信息的原理与应用[J]．大地测量与地球动力学，2002，22(2)：1-6．
[79] 李新武，郭华东，廖静娟等．航天飞机极化干涉雷达数据反演地表植被参数[J]．遥感学报，2002，6(6)：424-429．
[80] 刘国祥，丁晓利，李志林等．使用卫星雷达差分干涉技术测量香港赤腊角机场沉降场[J]．科学通报，2001，46(14)：1224-1228．
[81] 刘永坦．雷达成像技术[M]．哈尔滨：哈尔滨工业大学出版社，1999．
[82] 刘勇卫，贺雪鸿译．日本遥感研究会．遥感精解[M]．测绘出版社，1993，12．
[83] 马蔼乃．遥感概论[M]．科学出版社，1984．
[84] 乔书波，李金岭，孙付平等．InSAR技术现状与应用[J]．天文学进展，2003，21(1)：11-25．
[85] 潘析哲．星载SAR图像处理[M]．科学出版社，1996．
[86] 单新建，刘浩．利用干涉合成孔径雷达技术提取数字地面模型[J]．国土资源遥感，2001，2：43-47．
[87] 沈庭芝，方子文．数字图像处理及模式识别[M]．北京理工大学出版社，1998．
[88] 舒宁．雷达影像干涉测量原理[M]．武汉：武汉大学出版社，2003．
[89] 舒宁．微波遥感原理[M]．测绘出版社，2003．
[90] 肖国超，朱彩英．雷达摄影测量[M]．北京：地震出版社，2001．
[91] 许才军，王华，黄劲松．GPS与INSAR数据融合研究展望[J]．武汉大学学报，2003，28(特刊)：58-61．
[92] 徐永健，王贞松，罗小广．星载SAR数据成像的并行和实时处理研究—曙光机的应用实例[J]．电子与信息学报，2001，23(8)：736-743．
[93] 王超．利用航天飞机成像雷达干涉数据提取数字地面高程模型[J]．遥感学报，1997，1(1)：46-49．
[94] 王超，张红，刘智．星载合成孔径雷达干涉测量[M]．北京：科学出版社，2002．
[95] 王超，张红，于勇等．雷达差分干涉测量[J]．地理学与国土研究，2002，18(3)：13-16．
[96] 乌拉比F.T.[美]，穆尔R.K，冯健超著，黄培康，汪一飞译，微波遥感(第二卷)—雷达遥感和面目标的散射、辐射理论[M]．科学出版社，1987．
[97] 叶叔华，黄珹．天文地球动力学[M]．济南：山东科学技术出版社，2001．
[98] 于大洋，董贵威，杨健等．基于干涉极化SAR数据的森林树高反演[J]．清华大学学报(自然科学版)，2005，45(3)：334-336．
[99] 袁孝康．星载合成孔径雷达导论[M]，北京：国防工业出版社，2003．1．
[100] 张祖勋，张剑清．数字摄影测量学[M]．武汉测绘科技大学出版社，1996．
[101] 赵广信，李兰，常耀广．辽宁省抚顺市区地质灾害监测系统设计方案[J]．中国地质灾害与防治学报，2003，14(3)：100-105．
[102] 周广森 译，郑家声 校，[苏]顿斯科夫等，雷达摄影测量[M]．测绘出版社，1984．
[103] 朱述龙，张占睦编著．遥感影像获取与分析[M]．科学出版社，2000．
[2] Boerner W M, Cloude S R, Lee J S, et al. Advances in Extra Wide-Band Multi-modal Air/Space-borne Radar Polarimetry, POL-IN-SAR Imaging and its Applications [J]. IEEE Transactions on Geoscience and Remote Sensing, 2002:408-410.
[3] Bonforte A, Ferretti A, Prati C, et al. Calibration of atmospheric effects on SAR interferograms by GPS and local atmosphere models: first results [J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2001, 63:1343-1357.
[4] Brandfass M. Generation of Bald Earth Digital Elevation Models as Applied to Polarimetric SAR interferometry [A]. In: IGARSS'02. IEEE [C]. Toronto, Canada, 2002.2:1014-1016.
[5] Carnec C, Delacourt C. Three years of mining subsidence monitored by SAR interferometry, near Gardanne, France[J]. Journal of Geophysics. 2000. 43:43-54
[6] Cloude S R, Papathanassiou K P. Polarimetric SAR interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing. 1998, 36(5): 1551-1565.
[7] Cloude S R. Robust Parameters Estimation using Dual Baseline Polarimetric SAR Interferometry [A], In:IGRASS'02, IEEE [C]. Toronto. Canada. 2002, 2:838-840.
[8] Colesanti C. Ferretti A, Prati C, et al. Monitoring landslides and tectonic motions with the Permanent Scatterers Technique [J]. Engineering Geology, 2003, 68: 3-14.
[9] Colesanti C, Ferretti A, Prati C, et al. Multi-image satellite SAR interferometry: state of the art and future trends [A]. In: Proceedings of the International Radar Conference. IEEE [C]. Toulouse, France. 2003:239-244.
[10] Crosseto M, Tscherning C, Crippa B, et al. Subsidence Monitoring using SAR interferometry: Reduction of the atmospheric effects using stochastic filtering [J]. Geophysical Reaearch Letters, 2002, 29(9). 26-29.
[11] Ding. X. L., Li, Z. W., Huang, C., and Zhu, J. J., Selection of Filtering Parameter for the Goldstein Radar Interferogram Filter[J](submitted). IEEE Transactions on Geoscience and Remote Sensing. 2006.
[12] Eineder M, Adam N. Avoiding phase unwrapping in DEM generation by fusing multi frequency ascending and descending interferograms [A]. In: Proceedings of IGARS'04, IEEE[C]. Alaska, USA, 2004: 477-480.
[13] Ferretti A, Guarnieri A M, Prati C, et al. Multibaseline Interferometric Techniques and Applications [A]. In: Fringe 96' Workshop on ERS SAR interferometry[C]. Zurich, Switzerland, 1997: 17-21.
[14] Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry. In: Proceedings of IGARSS'99, IEEE [C]. Hamburg, Germany, 1999, 3: 1528-1530.
[15] Ferretti A, Prati C, Rocca F.Nonlinear Subsidence Rate Estimation Using Permanent Scatterers in Differential SAR Interferometry [J]. IEEE Transactions on GeoScience and Remote Sensing, 2000, 38(5): 2202-2212.
[16] Ferretti A, Prati C, Rocca F. Permanent scatterers in SAR interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20.2004, 29 (8): 664-668.
[17] Fetterer F, Gineris D, Wackerman C. Validating a scatterometer wind algorithm for ERS-1 SAR[J]. IEEE Trans. On Geosciences and Remote Sensing, 1998, 36(2): 479-492.
[18] Gabriel A K, Goldstein R M, Zebker H A. Mapping small elevation changes over large areas: Differential radar interferometry [J]. Journal of Geophysical Research, 1989, 94(B7): 9183-9191.
[19] Gatellif F., Guarnieri A M, Parizzi F, Pasquali P, Prati C, Rocca F. The wavenumber shift in SAR interferometry, IEEE transactions on Geosciences and Remote Sensing, 1994, 32(4): 855-865.
[20] Ge L L, Chang H C, Rizos C, Omura M. Mine Subsidence Monitoring: a Comparison among ENVISAT, ERS and JERS-1, Proceedings of 2004 ENVISAT Symposium[C], Salzburg, Austria, 6-10 September, Session 3: 4-9.
[21] Ge L L, Rizos C, Han S W, Zebker H. Mining Subsidence Monitoring Using the Combined InSAR and GPS Approach [C]. The 10th FIG international symposium on deformation measurements, California: Orange, 2001.
[22] Ghiglia D C, Wahl D E. Interferometric synthetic aperture radar terrain elevation mapping from multiple observations [A]. In: Proceedings of the 1994 Sixth IEEE Digital Signal Processing Workshop [C]. Yoseminte National Park, Canada, 1994: 33-36.
[23] Goldstein R M, Zebker H A. Interferometric radar measurement of ocean surface currents [J]. Nature, 1987, 328: 707-709.
[24] Goldstein R M, Zebker H A, Werner C L. Satellite radar interferometry: Two dimensional phase unwrapping [J]. Radio Science, 1988, 23(4): 713-720.
[25] Goldstein R. M. and Werner, C. L., Radar Interferogram Filtering for Geophysical Applications [J]. Geophysical Research Letter, 1998, 25(21): 4035-4038.
[26] Goldstein R. Atmospheric limitations to repeat-track radar interferometry [J]. Geophysical Research Letter, 1995, 22(18): 2517-2520.
[27] Graham L C, Synthetic Interferometer Radar for Topographic Mapping[J]. Proceedings of IEEE, 1974, 62(6): 763-768.
[28] Hajnsek I, Cloude S R. Pol-InSAR for agricultural vegetation parameter estimation[A]. In: Proceedings of IGARSS'04, IEEE [C]. Anchorage, USA, 2004, 2: 1224-1227.
[29] Hanssen R.F, Wechwerth T M, Zebker H A, et al. High-resolution water vapor mapping from interferometric radar measurements [J]. Science, 1999, 283: 1297-1299.
[30] Harding D J, Blair J B, Rodriguez E, et al. Airborne laser altimetry and interferometric SAR measurements of canopy structure and sub- canopy topography in the Pacific Northwest [A]. In: Proceedings of Second Symposium on Combined Optical-Microwave Earth and Atmosphere Sensing(COMEAS) [C]. Piscataway, New Jersey, USA, 1995: 22-24.
[31] Hassen R. Atmospheric heterogeneities in ERS tandem SAR interferometry [M]. Delft: Delft University Press, 1998.
[32] Hanssen R F. Radar Interferometry: Data Interpretation and Error Analysis[M]. Kluwer Academic Publishers, Dordercht, The Netherlands, 2001.
[33] Hersbach H. CMOD5—An improved geophysical model function for ERS C-band scatterometry. European Center for Medium Range Weather Forecasts, internal report[EB/OL], 2003.
[34] Homer J, Longstaff I D, Callaghan G. High resolution 3-D SAR via multi, baseline interferometry [A]. In: Proceedings of IGARSS'96, IEEE [C]. Lincoln, Nebraska, USA, 1996: 796-798.
[35] Horstmann J, Koch W, Lehner S, et al. Wind Retrieval Over The Ocean Using Synthetic Aperture radar with C-band HH Polarization [J]. IEEE Trans. On Geosciences and Remote Sensing, 2000, 38(5): 2122-2131.
[36] Horstmann J, Schiller H, Schulz-Stellenfleth J, et al. Global wind speed retrieval from SAR[J]. IEEE Trans. On Geosciences and Remote sensing, 2003, 41 (10): 2277-2286.
[37] InSAR Project Description of Caltech's Center for Advanced Computing Research [EB/OL]. http://www.cacr.caltech.edu/SDA/InSAR/insardesc.html.
[38] Jarosz A, Wanke D. Use of InSAR for Monitoring of Mining Deformations[C]. Proc. of FRINGE 2003 Workshop, Frascati. Italy, 1-5 December 2003, ESA SP-550, Jun 2004.
[39] Johannessen J A, Vachon P W, Johannessen O M. ERS-1 SAR imaging of marine boundary layer processes[C]. ESA, Earth Observation Quarterly. Paris, France, 1994a, 46: 1-256.
[40] Jong-sen Lee, Cloude S R, Papathanassiou K P, et al. Speckle filtering and coherence estimation of polarimetric SAR interferometry data for forest applications [J]. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41 (10): 2254-2263.
[41] Just D. Bamler Phase statistics of interfergrams with applications to synthetic aperture rasar[J], Appl. Optics, 1994, 33(20): 4361-4368.
[42] Kerbaol V, Chapron B, Vachon P.W. Analysis of ERS-1/2 Synthetic Aperture Radar Wave Mode Imagettes [J]. Journal of Geophysical Research, 1998, 103(C4): 7833-7846.
[43] Kim Y J, Zyl J V. Overview of Polarimetric Interferometry [A]. In: Proceedings of IEEE 2000 Aerospace Conference [C]. Big Sky, Montana, USA, 2000, 3: 231-236.
[44] Korsbakken E, Johannessen J A, Johannessen O M. Coastal Wind Field Retrievals from ERS Synthetic Aperture Radar Imagesp[J]. Journal of Geophysical Research, 1998, 103(C4): 7857-7874.
[45] Li T, Liu J N, Liao M S, Kuang S J, Lu X. Monitoring city subsidence by D-InSAR in Tianjin area [A]. In: Proceedings of IGARSS'04, IEEE [C]. Anchorage, USA, 2004, 5: 3333-3336.
[46] Li X W, Guo H D, Wang C L, et al. Generation and Error Analysis of DEM Using Spaceborne Polarimetric SAR Interferometry Data [A]. In: IGARSS'02, IEEE [C]. Toronto, Canada, 2002, 5: 2705-2707.
[47] Li Z L, Zou W B, Ding X. L., Chen. Y Q, and Liu G X. A Quantitative Measure for the Quality of InSAR Interferograms Based on Phase Differences[J]. Photogrammetric Engineering & Remote Sensing, 2004, 70(10): 1131-1137.
[48] Li Z W, Ding X L, Liu G X, et al. Atmospheric effects on InSAR measurements-a review [J]. Geomatics Research Australasia, 2003c, 79: 43-58.
[49] Li Z W, Ding X L, Liu G X. Modeling atmospheric effects on InSAR with meteorological and continuous GPS observations: algorithms and some test results [J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2004a, 66: 907-917.
[50] Living on a restless planet. Solid Earth science working group report 2002 [EB/OL]. http://solidearth.jpl.nasa.gov.
[51] Luciano V D, Marcos T E, Britaldo S S, et al. Assessment of Digital Elevation Models Obtained in Brazilian Amazon Based on P and X band airborne Interferometric Data [A]. In: IGARSS'02, IEEE [C]. Toronto, Canada, 2002: 3617-3619.
[52] Massonnet D, Rossi M, Carmona C, et al. The displacement field of the Landers earthquake mapped by radar interferometry [J]. Nature, 1993, 364(8): 138-142.
[53] Massonnet D, Feigl K L. Discrimination of geophysical phenomena in satellite radar interferograms [J]. Geophysical Research Letters, 1995, 22(12): 1537-1540.
[54] Nagler T, Rott H, Kamelger A. Analysis of landslides in Alpine areas by means of SAR interferometry [A]. In: Proceedings of IGARSS'02, IEEE [C]. Toronto, Canada, 2002, 1: 198-200.
[55] Nico G, Pappalepore M, Pasquariello G, et al. Comparison of ERS-1 SAR Data VS. coherence for flood detection methods-A GIS application [J]. International. Journal of Remote Sensing, 2000, 21 (8): 1619-1631.
[56] Nutricato R, Bovenga F, Refice A. Optimum interpolation and resampling for PSC identification [A]. In: Proceedings of IGARSS'02, IEEE [C]. Toronto, Canada, 2002, 6: 3626-3628.
[57] Papathanassiou K P, Moreira J R. Interferometric analysis of multifrequency and multipolarization SAR data [A]. In: Proceedings of IGARSS'96, IEEE[C]. Lincoln, Nebraska, USA, 1996: 1227-1229.
[58] Papathanassiou K P, Cloude S R. Polarimetric effects in repeat-pass SAR interferometry [A]. In: IGARSS'97, IEEE [C]. Singapore, 1997, 4: 1926-1928.
[59] Papathanassiou K P, Cloude S R. Single-Baseline Polarimetric SAR Interferometry [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2352-2363.
[60] Perski, Z., and Jura, D., ERS SAR Interferometry for Land Subsidence Detection in Coal Mining Areas [J]. EOQ 2001, 63: 21-25.
[61] Portabella M, Stoffelen A, Johannessen J A. Toward an Optimal Inversion Method for Synthetic Aperture Radar Wind Retrieval [J]. Journal of Geophysical Research, 2002.. 107(C8): 1-13.
[62] Rosen P A, Hensley S, Joughin I R, et al. Synthetic aperture radar interferometry [J]. Proceedings of IEEE, 2000, 88(3): 333-382.
[63] Shum C. K. Distributed Computing to Support Interdisciplinary Research in Space Geodesy and Earth Sciences Using the OSC 16-node SGI Cluster [EB/OL]. http://www.osc.edu/hpc/cluster_ohio/sites/rev2/osus.index.shtml.
[64] Slatton K C, Evans B L. Fusing interferometric radar and laser altimeter data to estimate surface topography and vegetation heights [J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2470-2482.
[65] Stoffelen A, Anderson D L T. Scatterometer data interpretation: Estimation and validation of the transfer function CM0D4 [J]. Journal of Geophysical Research, 1997,102(C3): 5767-5780.
[66] Tabb M, Flynn T, Carande R. An Extended Model for Characterizing Vegetation Canopies Using Polarimetric SAR Interferometry [A]. In: IGARSS'02, IEEE [C], Toronto, Canada, 2002:1020-1022.
[67] Thompson D R, Beal R C. Mapping High-Resolution Wind Fields Using Synthetic Aperture Radar[J]. J. Hopkins APL Tech. Digest, 2000, 21 (1):58-67
[68] Timmen L, Ye X, Reigber C. Monitoring of Small Motions in Mining Areas by SAR Interferometry [EB/OL]. http://earth.esa.int/workshops/fringe_1996/timmen/.
[69] Vachon P W, Dobson F W. Validation of Wind Vector Retrieval from ERS-1 SAR Images over the Ocean [J]. Global Atmos. Ocean Syst, 1996, 5(2): 177-187
[70] Wackermann C C, Rufenach C L, Shuchman R A, et al. Wind Vector Retrieval Using ERS-1 Synthetic Aperture Radar Imagery[J]. IEEE Trans. On Geosciences and Remote Sensing, 1996, 34(6): 1343-1352.
[74] Williams S, Bock Y, Fang P. Integrated satellite interferometry: tropospheric noise, GPS estimates and implications for interferometric synthetic aperture radar product [J]. Journal of Geophysical Research, 1998,103(B11): 27051-27067.
[72] Webley P W, Bingley R M, Dodson A H, et al. Atmospheric water vapor correction to InSAR surface motion measurements on mountains: results from a dense GPS network on Mount Etna [J]. Physics and Chemistry of the Earth, 2002, 27:363-370.
[73] Yijun He, William Perrie, Qingping Zou, et al. A New Wind Vector Algorithm for C-band SAR [J]. IEEE Trans. On Geosciences and Remote sensing. 2005, 43(7): 1453-1458.
[74] Zebker H A, Rosen P A, Hensley S. Atmospheric effects in interferometric synthetic aperture radar surface deformation and topographic maps [J]. Journal of Geophysical Research, 1997, 102(B4): 7547-7563.
[75] Zebker H.A, Villasenor J, Decorrelation in Interferometric Radar Echoes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1992, 30: 950-959.
[76] 蔡希尧．雷达系统概论[M]，科学出版社：1983．
[77] 李德仁，廖明生，王艳．永久散射体雷达干涉测量技术[J]．武汉大学学报(信息科学版)，2004，29(8)：664-668．
[78] 李德仁，杨杰．从卫星雷达提取地面高程信息的原理与应用[J]．大地测量与地球动力学，2002，22(2)：1-6．
[79] 李新武，郭华东，廖静娟等．航天飞机极化干涉雷达数据反演地表植被参数[J]．遥感学报，2002，6(6)：424-429．
[80] 刘国祥，丁晓利，李志林等．使用卫星雷达差分干涉技术测量香港赤腊角机场沉降场[J]．科学通报，2001，46(14)：1224-1228．
[81] 刘永坦．雷达成像技术[M]．哈尔滨：哈尔滨工业大学出版社，1999．
[82] 刘勇卫，贺雪鸿译．日本遥感研究会．遥感精解[M]．测绘出版社，1993，12．
[83] 马蔼乃．遥感概论[M]．科学出版社，1984．
[84] 乔书波，李金岭，孙付平等．InSAR技术现状与应用[J]．天文学进展，2003，21(1)：11-25．
[85] 潘析哲．星载SAR图像处理[M]．科学出版社，1996．
[86] 单新建，刘浩．利用干涉合成孔径雷达技术提取数字地面模型[J]．国土资源遥感，2001，2：43-47．
[87] 沈庭芝，方子文．数字图像处理及模式识别[M]．北京理工大学出版社，1998．
[88] 舒宁．雷达影像干涉测量原理[M]．武汉：武汉大学出版社，2003．
[89] 舒宁．微波遥感原理[M]．测绘出版社，2003．
[90] 肖国超，朱彩英．雷达摄影测量[M]．北京：地震出版社，2001．
[91] 许才军，王华，黄劲松．GPS与INSAR数据融合研究展望[J]．武汉大学学报，2003，28(特刊)：58-61．
[92] 徐永健，王贞松，罗小广．星载SAR数据成像的并行和实时处理研究—曙光机的应用实例[J]．电子与信息学报，2001，23(8)：736-743．
[93] 王超．利用航天飞机成像雷达干涉数据提取数字地面高程模型[J]．遥感学报，1997，1(1)：46-49．
[94] 王超，张红，刘智．星载合成孔径雷达干涉测量[M]．北京：科学出版社，2002．
[95] 王超，张红，于勇等．雷达差分干涉测量[J]．地理学与国土研究，2002，18(3)：13-16．
[96] 乌拉比F.T.[美]，穆尔R.K，冯健超著，黄培康，汪一飞译，微波遥感(第二卷)—雷达遥感和面目标的散射、辐射理论[M]．科学出版社，1987．
[97] 叶叔华，黄珹．天文地球动力学[M]．济南：山东科学技术出版社，2001．
[98] 于大洋，董贵威，杨健等．基于干涉极化SAR数据的森林树高反演[J]．清华大学学报(自然科学版)，2005，45(3)：334-336．
[99] 袁孝康．星载合成孔径雷达导论[M]，北京：国防工业出版社，2003．1．
[100] 张祖勋，张剑清．数字摄影测量学[M]．武汉测绘科技大学出版社，1996．
[101] 赵广信，李兰，常耀广．辽宁省抚顺市区地质灾害监测系统设计方案[J]．中国地质灾害与防治学报，2003，14(3)：100-105．
[102] 周广森 译，郑家声 校，[苏]顿斯科夫等，雷达摄影测量[M]．测绘出版社，1984．
[103] 朱述龙，张占睦编著．遥感影像获取与分析[M]．科学出版社，2000．