| Citation: |
YANG Mengshi, LIAO Mingsheng, CHANG Ling, HANSSEN Ramon F.. Interpretation of Multi-epoch InSAR Deformation for Urban Scenes: A Problem Analysis and Literature Review[J]. Geomatics and Information Science of Wuhan University, 2023, 48(10): 1643-1660. DOI: 10.13203/j.whugis20230289
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Multi-epoch interferometric synthetic aperture radar (InSAR) is a highly effective technique for monitoring deformation in urban areas. However, interpreting InSAR deformation can be challenging due to various factors, including inherent geometric imaging distortion, the intricate structure and deformation properties of targets in urban scenes, and the multiple scattering of microwave signals between objects in urban scenes. This paper discusses the challenges involved in interpreting time-series InSAR deformation: (1) Precisely identifying the location of deformation signals and linking them to their corresponding objects, i.e., determining where the deformation signal occurs, (2) understanding the mechanisms and factors that cause the detected deformation signals, i.e., determining what the deformation signal represents, (3) establishing the connection among the detected deformation signals, the deformation events, and the scattering mechanisms. We suggest a parametric framework to improve the accurate interpretation of InSAR deformation. This framework includes several factors, including kinematic characteristics (deformation rate, cumulative deformation, deformation gradient, and deformation model), geometric parameters (position, size, structure, orientation, and roughness), semantic information (land cover type, terrain morphology, texture, and auxiliary information on natural and anthropogenic disturbance) and physical properties (scattering mechanism, penetrability, extensibility, conductivity, and thermal conductivity). Our approach aims to enhance the representation of coherent points for a better understanding of InSAR deformation. This paper offers a comprehensive overview of the advancements achieved in extracting parameters of InSAR coherent points and interpreting deformation based on geometric parameters, semantic information, and physical properties. High-precision 3D positioning is crucial for InSAR fine monitoring in urban areas. It helps determine the source of deformation signals and facilitates the analysis of deformation mechanisms. Semantic information, such as 3D models, high-resolution optical images, laser point cloud data, and land use data, can aid in interpreting InSAR deformation. By combining InSAR deformation data with a deep learning approach, there is an opportunity to interpret deformations effectively. In urban environments, the scattering mechanism of ground objects is complex. Multiple scattering signals can provide effective observations of deformation and information about the target's size. However, combining the scattering mechanism of synthetic aperture radar signals to carry out parameter inversion and deformation mechanism interpretation of urban target terrain remains a challenge. The framework, which considers the geometric parameters, semantic information, and physical attributes of InSAR coherent points, will be crucial for deformation interpretation and mechanism cognition. This framework will enable fine deformation monitoring, intelligent recognition, and application in future urban scenes.
| [1] |
Ferretti A, Prati C, Rocca F. Permanent Scatterers in SAR Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20. doi: 10.1109/36.898661
|
| [2] |
Berardino P, Fornaro G, Lanari R, et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383. doi: 10.1109/TGRS.2002.803792
|
| [3] |
Hooper A, Zebker H, Segall P, et al. A New Method for Measuring Deformation on Volcanoes and Other Natural Terrains Using InSAR Persistent Scatterers[J]. Geophysical Research Letters, 2004, 31(23): L23611.
|
| [4] |
Kampes B M. Radar Interferometry: Persistent Scatterer Technique[M]. Dordrecht, the Netherlands: Springer, 2006.
|
| [5] |
Ferretti A, Fumagalli A, Novali F, et al. A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3460-3470. doi: 10.1109/TGRS.2011.2124465
|
| [6] |
Minh D, Hanssen R, Rocca F. Radar Interferometry: 20 Years of Development in Time Series Techniques and Future Perspectives[J]. Remote Sensing, 2020, 12(9): 1364. doi: 10.3390/rs12091364
|
| [7] |
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. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200408001.htm
Li Deren, Liao Mingsheng, Wang Yan. Progress of Permanent Scatterer Interferometry[J]. Geomatics and Information Science of Wuhan University, 2004, 29(8): 664-668. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH200408001.htm
|
| [8] |
Guo L, Gong H, Li J, et al. Understanding Uneven Land Subsidence in Beijing, China, Using a Novel Combination of Geophysical Prospecting and InSAR[J]. Geophysical Research Letters, 2020, 47(16): e2020GL088676. doi: 10.1029/2020GL088676
|
| [9] |
Dong J, Guo S, Wang N, et al. Tri-decadal Evolution of Land Subsidence in the Beijing Plain Revealed by Multi-Epoch Satellite InSAR Observations[J]. Remote Sensing of Environment, 2023, 286: 113446. doi: 10.1016/j.rse.2022.113446
|
| [10] |
Luo Q L, Perissin D, Zhang Y Z, et al. L- and X-band Multi-temporal InSAR Analysis of Tianjin Subsidence[J]. Remote Sensing, 2014, 6(9): 7933-7951. doi: 10.3390/rs6097933
|
| [11] |
Shi X G, Zhu T T, Tang W, et al. Inferring Decelerated Land Subsidence and Groundwater Storage Dynamics in Tianjin–Langfang Using Sentinel-1 InSAR[J]. International Journal of Digital Earth, 2022, 15(1): 1526-1546. doi: 10.1080/17538947.2022.2122610
|
| [12] |
Wang R, Yang M S, Yang T L, et al. Decomposing and Mapping Different Scales of Land Subsidence over Shanghai with X- and C-Band SAR Data Stacks[J]. International Journal of Digital Earth, 2022, 15(1): 478-502. doi: 10.1080/17538947.2022.2036835
|
| [13] |
廖明生, 裴媛媛, 王寒梅, 等. 永久散射体雷达干涉技术监测上海地面沉降[J]. 上海国土资源, 2012, 33(3): 5-10. https://www.cnki.com.cn/Article/CJFDTOTAL-SHAD201203005.htm
Liao Mingsheng, Pei Yuanyuan, Wang Hanmei, et al. Subsidence Monitoring in Shanghai Using the PSInSAR Technique[J]. Shanghai Land & Resources, 2012, 33(3): 5-10. https://www.cnki.com.cn/Article/CJFDTOTAL-SHAD201203005.htm
|
| [14] |
王艳, 廖明生, 李德仁, 等. 利用长时间序列相干目标获取地面沉降场[J]. 地球物理学报, 2007, 50(2): 598-604. doi: 10.3321/j.issn:0001-5733.2007.02.034
Wang Yan, Liao Mingsheng, Li Deren, et al. Subsidence Velocity Retrieval from Long-term Coherent Targets in Radar Interferometric Stacks[J]. Chinese Journal of Geophysics, 2007, 50(2): 598-604. doi: 10.3321/j.issn:0001-5733.2007.02.034
|
| [15] |
张勤, 赵超英, 丁晓利, 等. 利用GPS与InSAR研究西安现今地面沉降与地裂缝时空演化特征[J]. 地球物理学报, 2009, 52(5): 1214-1222. doi: 10.3969/j.issn.0001-5733.2009.05.010
Zhang Qin, Zhao Chaoying, Ding Xiaoli, et al. Research on Recent Characteristics of Spatio-temporal Evolution and Mechanism of Xi'an Land Subsidence and Ground Fissure by Using GPS and InSAR Techniques[J]. Chinese Journal of Geophysics, 2009, 52(5): 1214-1222. doi: 10.3969/j.issn.0001-5733.2009.05.010
|
| [16] |
Li G, Zhao C, Wang B, et al. Evolution of Spatiotemporal Ground Deformation over 30 Years in Xi'an, China, with Multi-sensor SAR Interferometry[J]. Journal of Hydrology, 2023, 616: 128764. doi: 10.1016/j.jhydrol.2022.128764
|
| [17] |
Tang W, Liao M. Taiyuan City Subsidence Observed with Persistent Scatterer InSAR[J]. Wuhan University Journal of Natural Sciences, 2014, 19(6): 526-534. doi: 10.1007/s11859-014-1048-7
|
| [18] |
Liu Y, Zhao C, Zhang Q, et al. Land Subsidence in Taiyuan, China, Monitored by InSAR Technique With Multisensor SAR Datasets From 1992 to 2015[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(5): 1509-1519. doi: 10.1109/JSTARS.2018.2802702
|
| [19] |
Ng A, Wang H, Dai Y, et al. InSAR Reveals Land Deformation at Guangzhou and Foshan, China Between 2011 and 2017 with COSMO-SkyMed Data[J]. Remote Sensing, 2018, 10(6): 813. doi: 10.3390/rs10060813
|
| [20] |
Sun M, Du Y, Liu Q, et al. Understanding the Spatial-Temporal Characteristics of Land Subsidence in Shenzhen Under Rapid Urbanization Based on MT-InSAR[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 4153-4166. doi: 10.1109/JSTARS.2023.3264652
|
| [21] |
Xu B, Feng G, Li Z, et al. Coastal Subsidence Monitoring Associated with Land Reclamation Using the Point Target Based SBAS-InSAR Method: A Case Study of Shenzhen, China[J]. Remote Sensing, 2016, 8(8): 652-670. doi: 10.3390/rs8080652
|
| [22] |
白林, 江利明, 汪汉胜. 利用高分辨率TerraSAR-X数据监测武汉地区2013—2015年地面沉降[J]. 大地测量与地球动力学, 2019, 39(8): 832-836. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201908013.htm
Bai Lin, Jiang Liming, Wang Hansheng. Monitoring Ground Subsidence in Wuhan City with High-Resolution TerraSAR-X Images from 2013 to 2015[J]. Journal of Geodesy and Geodynamics, 2019, 39(8): 832-836. https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201908013.htm
|
| [23] |
Shi X, Zhang S, Jiang M, et al. Spatial and Temporal Subsidence Characteristics in Wuhan (China), During 2015–2019, Inferred from Sentinel-1 Synthetic Aperture Radar (SAR) Interferometry[J]. Natural Hazards and Earth System Sciences, 2021, 21(8): 2285-2297. doi: 10.5194/nhess-21-2285-2021
|
| [24] |
Zhang Y, Zhang J, Wu H, et al. Monitoring of Urban Subsidence with SAR Interferometric Point Target Analysis: A Case Study in Suzhou, China[J]. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(5): 812-818. doi: 10.1016/j.jag.2011.05.003
|
| [25] |
Shi G, Ma P, Hu X, et al. Surface Response and Subsurface Features During the Restriction of Groundwater Exploitation in Suzhou (China) Inferred from Decadal SAR Interferometry[J]. Remote Sensing of Environment, 2021, 256: 112327. doi: 10.1016/j.rse.2021.112327
|
| [26] |
Zhou C, Lan H, Bürgmann R, et al. Application of an Improved Multi-temporal InSAR Method and Forward Geophysical Model to Document Subsi⁃dence and Rebound of the Chinese Loess Plateau Following Land Reclamation in the Yanan New District[J]. Remote Sens Environ, 2022, 279: 113102. doi: 10.1016/j.rse.2022.113102
|
| [27] |
Wang Y D, Feng G C, Li Z W, et al. Estimating the Long-term Deformation and Permanent Loss of Aquifer in the Southern Junggar Basin, China, Using InSAR[J]. Journal of Hydrology, 2022, 614: 128604. doi: 10.1016/j.jhydrol.2022.128604
|
| [28] |
Li P, Wang G, Liang C, et al. InSAR-Derived Coastal Subsidence Reveals New Inundation Scenarios over the Yellow River Delta[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, DOI: 10.1109/JSTARS.2023.32724782.
|
| [29] |
Zhao Q, Lin H, Gao W, et al. InSAR Detection of Residual Settlement of an Ocean Reclamation Engineering Project: A Case Study of Hong Kong International Airport[J]. Journal of Oceanography, 2011, 67(4): 415-426. doi: 10.1007/s10872-011-0034-3
|
| [30] |
Jiang Y N, Liao M S, Wang H M, et al. Deformation Monitoring and Analysis of the Geological Environment of Pudong International Airport with Persistent Scatterer SAR Interferometry[J]. Remote Sensing, 2016, 8(12): 1021. doi: 10.3390/rs8121021
|
| [31] |
Marshall C, Large D J, Athab A, et al. Monitoring Tropical Peat Related Settlement Using ISBAS InSAR, Kuala Lumpur International Airport (KLIA)[J]. Engineering Geology, 2018, 244: 57-65. doi: 10.1016/j.enggeo.2018.07.015
|
| [32] |
Bianchini Ciampoli L, Gagliardi V, Ferrante C, et al. Displacement Monitoring in Airport Runways by Persistent Scatterers SAR Interferometry[J]. Remote Sensing, 2020, 12(21): 3564. doi: 10.3390/rs12213564
|
| [33] |
Wu S B, Yang Z F, Ding X L, et al. Two Decades of Settlement of Hong Kong International Airport Measured with Multi-temporal InSAR[J]. Remote Sensing of Environment, 2020, 248: 111976. doi: 10.1016/j.rse.2020.111976
|
| [34] |
An B, Jiang Y, Wang C, et al. Ground Infrastructure Monitoring in Coastal Areas Using Time-Series InSAR Technology: The Case Study of Pudong International Airport, Shanghai[J]. International Journal of Digital Earth, 2023, 16(1): 2171144. doi: 10.1080/17538947.2023.2171144
|
| [35] |
Huang Q, Crosetto M, Monserrat O, et al. Displacement Monitoring and Modelling of a High-Speed Railway Bridge Using C-band Sentinel-1 Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 128: 204-211. doi: 10.1016/j.isprsjprs.2017.03.016
|
| [36] |
Lazecky M, Hlavacova I, Bakon M, et al. Bridge Displacements Monitoring Using Space-Borne X-Band SAR Interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(1): 205-210. doi: 10.1109/JSTARS.2016.2587778
|
| [37] |
Peduto D, Elia F, Montuori R. Probabilistic Analysis of Settlement-Induced Damage to Bridges in the City of Amsterdam (The Netherlands)[J]. Transportation Geotechnics, 2018, 14: 169-182. doi: 10.1016/j.trgeo.2018.01.002
|
| [38] |
Qin X Q, Zhang L, Yang M S, et al. Mapping Surface Deformation and Thermal Dilation of Arch Bridges by Structure-Driven Multi-temporal DInSAR Analysis[J]. Remote Sensing of Environment, 2018, 216: 71-90. doi: 10.1016/j.rse.2018.06.032
|
| [39] |
Ma P F, Li T, Fang C Y, et al. A Tentative Test for Measuring the Sub-millimeter Settlement and Uplift of a High-Speed Railway Bridge Using COSMO-SkyMed Images[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 155: 1-12. doi: 10.1016/j.isprsjprs.2019.06.013
|
| [40] |
Milillo P, Giardina G, Perissin D, et al. Pre-Collapse Space Geodetic Observations of Critical Infrastructure: The Morandi Bridge, Genoa, Italy[J]. Remote Sensing, 2019, 11(12): 1403-1403. doi: 10.3390/rs11121403
|
| [41] |
Xiong S T, Wang C S, Qin X Q, et al. Time-series Analysis on Persistent Scatter-Interferometric Synthetic Aperture Radar (PS-InSAR) Derived Displacements of the Hong Kong–Zhuhai–Macao Bridge (HZMB) from Sentinel-1A Observations[J]. Remote Sensing, 2021, 13(4): 546-558. doi: 10.3390/rs13040546
|
| [42] |
Chen F, Lin H, Li Z, et al. Interaction Between Permafrost and Infrastructure Along the Qinghai-Tibet Railway Detected via Jointly Analysis of C- and L-band Small Baseline SAR Interferometry[J]. Remote Sensing of Environment, 2012, 123: 532-540. doi: 10.1016/j.rse.2012.04.020
|
| [43] |
Chang L, Dollevoet R, Hanssen R. Nationwide Railway Monitoring Using Satellite SAR Interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(2): 596-604. doi: 10.1109/JSTARS.2016.2584783
|
| [44] |
Qin X Q, Liao M S, Zhang L, et al. Structural Health and Stability Assessment of High-speed Railways via Thermal Dilation Mapping with Time-Series InSAR Analysis[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10(6): 2999-3010. doi: 10.1109/JSTARS.2017.2719025
|
| [45] |
Xing X, Zhu Y, Xu W, et al. Measuring Subsidence over Soft Clay Highways Using a Novel Time-series InSAR Deformation Model with an Emphasis on Rheological Properties and Environmental Factors (NREM)[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-19.
|
| [46] |
Shi X, Jiang L, Jiang H, et al. Geohazards Analysis of the Litang⁃Batang Section of Sichuan⁃Tibet Railway Using SAR Interferometry[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 11998-12006. doi: 10.1109/JSTARS.2021.3129270
|
| [47] |
Perissin D, Wang Z, Lin H. Shanghai Subway Tunnels and Highways Monitoring Through Cosmo-SkyMed Persistent Scatterers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73: 58-67. doi: 10.1016/j.isprsjprs.2012.07.002
|
| [48] |
Li T, Liu G, Lin H, et al. Detecting Land Subsidence near Metro Lines in the Baoshan District of Shanghai with Multi-temporal Interferometric Synthetic Aperture Radar[J]. Journal of Modern Transportation, 2014, 22(3): 137-147. doi: 10.1007/s40534-014-0047-x
|
| [49] |
Wang H, Feng G, Xu B, et al. Deriving Spatio-temporal Development of Ground Subsidence due to Subway Construction and Operation in Delta Regions with PS-InSAR Data: A Case Study in Guangzhou, China[J]. Remote Sensing, 2017, 9(10): 1004. doi: 10.3390/rs9101004
|
| [50] |
Giardina G, Milillo P, Dejong M, et al. Evaluation of InSAR Monitoring Data for Post-Tunnelling Settlement Damage Assessment[J]. Structural Control and Health Monitoring, 2019, 26(2): e2285.
|
| [51] |
Macchiarulo V, Milillo P, DeJong M J, et al. Integrated InSAR Monitoring and Structural Assessment of Tunnelling-Induced Building Deformations[J]. Structural Control and Health Monitoring, 2021, 28(9): e2781.
|
| [52] |
Reinders K J, Hanssen R F, van Leijen F J, et al. Augmented Satellite InSAR for Assessing Short-Term and Long-Term Surface Deformation due to Shield Tunnelling[J]. Tunnelling and Underground Space Technology, 2021, 110: 103745. doi: 10.1016/j.tust.2020.103745
|
| [53] |
Wu S, Zhang B, Liang H, et al. Detecting the Deformation Anomalies Induced by Underground Construction Using Multiplatform MT-InSAR: A Case Study in to Kwa Wan Station, Hong Kong[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9803-9814. doi: 10.1109/JSTARS.2021.3113672
|
| [54] |
Wang R, Yang M, Dong J, et al. Investigating Deformation Along Metro Lines in Coastal Cities Considering Different Structures with InSAR and SBM Analyses[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 115: 103099. doi: 10.1016/j.jag.2022.103099
|
| [55] |
Yang M, Wang R, Li M, et al. A PSI Targets Characterization Approach to Interpreting Surface Displacement Signals: A Case Study of the Shanghai Metro Tunnels[J]. Remote Sensing of Environment, 2022, 280: 113150. doi: 10.1016/j.rse.2022.113150
|
| [56] |
Zeni G, Bonano M, Casu F, et al. Long-term Deformation Analysis of Historical Buildings Through the Advanced SBAS-DInSAR Technique: The Case Study of the City of Rome, Italy[J]. Journal of Geophysics and Engineering, 2011, 8(3): S1-S12. doi: 10.1088/1742-2132/8/3/S01
|
| [57] |
Gernhardt S, Bamler R. Deformation Monitoring of Single Buildings Using Meter-Resolution SAR Data in PSI[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73: 68-79. doi: 10.1016/j.isprsjprs.2012.06.009
|
| [58] |
Chang L, Hanssen R F. Detection of Cavity Migration and Sinkhole Risk Using Radar Interferometric Time Series[J]. Remote Sensing of Environment, 2014, 147: 56-64. doi: 10.1016/j.rse.2014.03.002
|
| [59] |
Ma P, Lin H, Lan H, et al. Multi-dimensional SAR Tomography for Monitoring the Deformation of Newly Built Concrete Buildings[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 106: 118-128. doi: 10.1016/j.isprsjprs.2015.04.012
|
| [60] |
Chen F, Wu Y, Zhang Y, et al. Surface Motion and Structural Instability Monitoring of Ming Dynasty City Walls by Two-step Tomo-PSInSAR Approach in Nanjing City, China[J]. Remote Sensing, 2017, 9(4): 371. doi: 10.3390/rs9040371
|
| [61] |
Wang Z, Balz T, Zhang L, et al. Using TSX/TDX Pursuit Monostatic SAR Stacks for PS-InSAR Analysis in Urban Areas[J]. Remote Sensing, 2018, 11(1): 26. doi: 10.3390/rs11010026
|
| [62] |
Liu P, Li Z, Wang C, et al. Phase Unmixing of TerraSAR-X Staring Spotlight Interferograms in Building Scale for PS Height and Deformation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 180: 14-28. doi: 10.1016/j.isprsjprs.2021.08.007
|
| [63] |
Ma P, Zheng Y, Zhang Z, et al. Building Risk Monitoring and Prediction Using Integrated Multi-temporal InSAR and Numerical Modeling Techniques[J]. International Journal of Applied Earth Observation and Geoinformation, 2022, 114: 103076. doi: 10.1016/j.jag.2022.103076
|
| [64] |
Bassoli E, Vincenzi L, Grassi F, et al. A Multi-temporal DInSAR-based Method for the Assessment of the 3D Rigid Motion of Buildings and Corresponding Uncertainties[J]. Journal of Building Engineering, 2023, 73: 106738. doi: 10.1016/j.jobe.2023.106738
|
| [65] |
邢孟道, 林浩, 陈溅来, 等. 多平台合成孔径雷达成像算法综述[J]. 雷达学报, 2019, 8(6): 732-757. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201906006.htm
Xing Mengdao, Lin Hao, Chen Jianlai, et al. A Review of Imaging Algorithms in Multi-Platform-Borne Synthetic Aperture Radar[J]. Journal of Radars, 2019, 8(6): 732-757. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201906006.htm
|
| [66] |
廖明生, 王茹, 杨梦诗, 等. 城市目标动态监测中的时序InSAR分析方法及应用[J]. 雷达学报, 2020, 9(3): 409-424. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202003003.htm
Liao Mingsheng, Wang Ru, Yang Mengshi, et al. Techniques and Applications of Spaceborne Time-Series InSAR in Urban Dynamic Monitoring[J]. Journal of Radars, 2020, 9(3): 409-424. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX202003003.htm
|
| [67] |
朱建军, 宋迎春, 胡俊, 等. 测绘大数据时代数据处理理论面临的挑战与发展[J]. 武汉大学学报(信息科学版), 2021, 46(7): 1025-1031. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202107007.htm
Zhu Jianjun, Song Yingchun, Hu Jun, et al. Challenges and Development of Data Processing Theory in the Era of Surveying and Mapping Big Data[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 1025-1031. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH202107007.htm
|
| [68] |
葛大庆, 戴可人, 郭兆成, 等. 重大地质灾害隐患早期识别中综合遥感应用的思考与建议[J]. 武汉大学学报(信息科学版), 2019, 44(7): 949-956. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907001.htm
Ge Daqing, Dai Keren, Guo Zhaocheng, et al. Early Identification of Serious Geological Hazards with Integrated Remote Sensing Technologies: Thoughts and Recommendations[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 949-956. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907001.htm
|
| [69] |
Dheenathayalan P, Small D, Schubert A, et al. High-Precision Positioning of Radar Scatterers[J]. Journal of Geodesy, 2016, 90(5): 403-422. doi: 10.1007/s00190-015-0883-4
|
| [70] |
Small D, Rosich B, Meier E, et al. Geometric Calibration and Validation of ASAR Imagery[C]//CEOS SAR Workshop, Ulm, Geimany, 2004.
|
| [71] |
Schubert A, Jehle M, Small D, et al. Influence of Atmospheric Path Delay on the Absolute Geolocation Accuracy of TerraSAR-X High-Resolution Products[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(2): 751-758. doi: 10.1109/TGRS.2009.2036252
|
| [72] |
Eineder M, Minet C, Steigenberger P, et al. Imaging Geodesy-Toward Centimeter-Level Ranging Accuracy with TerraSAR-X[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(2): 661-671. doi: 10.1109/TGRS.2010.2060264
|
| [73] |
Cong X, Balss U, Eineder M, et al. Imaging Geodesy-Centimeter-Level Ranging Accuracy with TerraSAR-X: An Update[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(5): 948-952. doi: 10.1109/LGRS.2012.2187042
|
| [74] |
Balss U, Gisinger C, Eineder M. Measurements on the Absolute 2-D and 3-D Localization Accuracy of TerraSAR-X[J]. Remote Sensing, 2018, 10(4): 656. doi: 10.3390/rs10040656
|
| [75] |
Schubert A, Miranda N, Geudtner D, et al. Sentinel-1A/B Combined Product Geolocation Accuracy[J]. Remote Sensing, 2017, 9(6): 607. doi: 10.3390/rs9060607
|
| [76] |
Gisinger C, Schubert A, Breit H, et al. In-Depth Verification of Sentinel-1 and TerraSAR-X Geolocation Accuracy Using the Australian Corner Reflector Array[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(2): 1154-1181. doi: 10.1109/TGRS.2019.2961248
|
| [77] |
Gernhardt S, Auer S, Eder K. Persistent Scatterers at Building Facades-Evaluation of Appearance and Localization Accuracy[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 100: 92-105. doi: 10.1016/j.isprsjprs.2014.05.014
|
| [78] |
周月琴, 郑肇葆, 李德仁, 等. SAR图像立体定位原理与精度分析[J]. 遥感学报, 1998, 4(2): 245-250. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB804.001.htm
Zhou Yueqin, Zheng Zhaobao, Li Deren, et al. Stereopair Positioning Algorithm for SAR Images and Its Accuracy Analysis ModelSAR[J]. Journal of Remote Sensing, 1998, 4(2): 245-250. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB804.001.htm
|
| [79] |
Gernhardt S, Cong X, Eineder M, et al. Geometrical Fusion of Multitrack PS Point Clouds[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(1): 38-42. doi: 10.1109/LGRS.2011.2159190
|
| [80] |
Gisinger C, Balss U, Pail R, et al. Precise Three-Dimensional Stereo Localization of Corner Reflectors and Persistent Scatterers with TerraSAR-X[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 1782-1802. doi: 10.1109/TGRS.2014.2348859
|
| [81] |
Duque S, Parizzi A, Zan F D, et al. Precise and Automatic 3D Absolute Geolocation of Targets Using only Two Long-Aperture SAR Acquisitions[C]. IEEE International Geoscience and Remote Sensing Symposium, Beijing, China, 2016.
|
| [82] |
Zhu X X, Montazeri S, Gisinger C, et al. Geodetic SAR Tomography[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 18-35. doi: 10.1109/TGRS.2015.2448686
|
| [83] |
Zhu X, Bamler R. Very High Resolution Spaceborne SAR Tomography in Urban Environment[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(12): 4296-4308. doi: 10.1109/TGRS.2010.2050487
|
| [84] |
Montazeri S, Rodríguez González F, Zhu X. Geocoding Error Correction for InSAR Point Clouds[J]. Remote Sensing, 2018, 10(10): 1523. doi: 10.3390/rs10101523
|
| [85] |
Van Zyl J J. Calibration of Polarimetric Radar Images Using only Image Parameters and Trihedral Corner Reflector Responses[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(3): 337-348. doi: 10.1109/36.54360
|
| [86] |
Sarabandi K, Chiu T C. Optimum Corner Reflectors for Calibration of Imaging Radars[J]. IEEE Transactions on Antennas and Propagation, 1996, 44(10): 1348-1361. doi: 10.1109/8.537329
|
| [87] |
Small D, Schubert A, Rosich B, et al. Geometric and Radiometric Correction of ESA SAR Products[C]//Envisat Symposium 2007, Montreux, Switzerland, 2007.
|
| [88] |
Shimada M, Isoguchi O, Tadono T, et al. PALSAR Radiometric and Geometric Calibration[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(12): 3915-3932. doi: 10.1109/TGRS.2009.2023909
|
| [89] |
Hanssen R F. Radar Interferometry: Data Interpretation and Error Analysis[M]. Boston: Kluwer Academic Publishers, 2001.
|
| [90] |
Shi X, Zhang L, Balz T, et al. Landslide Deformation Monitoring Using Point-Like Target Offset Tracking with Multi-mode High-Resolution TerraSAR-X Data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105: 128-140. doi: 10.1016/j.isprsjprs.2015.03.017
|
| [91] |
Xia Y, Kaufmann H, Guo X. Differential SAR Interferometry Using Corner Reflectors[C]//IEEE International Geoscience and Remote Sensing Symposium, Toronto, Canada, 2002.
|
| [92] |
Mahapatra P S, Samiei-Esfahany S, Van Der Marel H, et al. On the Use of Transponders as Coherent Radar Targets for SAR Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(3): 1869-1878. doi: 10.1109/TGRS.2013.2255881
|
| [93] |
Crosetto M, Monserrat O, Cuevas-González M, et al. Persistent Scatterer Interferometry: A Review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 115: 78-89. doi: 10.1016/j.isprsjprs.2015.10.011
|
| [94] |
Ferretti A, Savio G, Barzaghi R, et al. Submillimeter Accuracy of InSAR Time Series: Experimental Validation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(5): 1142-1153. doi: 10.1109/TGRS.2007.894440
|
| [95] |
Marinkovic P, Ketelaar G, Van Leijen F, et al. InSAR Quality Control: Analysis of Five Years of Corner Reflector Time Series[C]//The 5th International Workshop on ERS/Envisat SAR Interferometry (FRINGE 2007), Frascati, Italy, 2007.
|
| [96] |
Garthwaite M C. On the Design of Radar Corner Reflectors for Deformation Monitoring in Multi-frequency InSAR[J]. Remote Sensing, 2017, 9(7): 648. doi: 10.3390/rs9070648
|
| [97] |
Gisinger C, Willberg M, Balss U, et al. Differential Geodetic Stereo SAR with TerraSAR-X by Exploiting Small Multi-Directional Radar Reflectors[J]. Journal of Geodesy, 2017, 91(1): 53-67. doi: 10.1007/s00190-016-0937-2
|
| [98] |
Mahapatra P, Der Marel H V, Van Leijen F, et al. InSAR Datum Connection Using GNSS-Augmented Radar Transponders[J]. Journal of Geodesy, 2018, 92(1): 21-32. doi: 10.1007/s00190-017-1041-y
|
| [99] |
Yang M, López-Dekker P, Dheenathayalan P, et al. On the Value of Corner Reflectors and Surface Models in InSAR Precise Point Positioning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158: 113-122. doi: 10.1016/j.isprsjprs.2019.10.006
|
| [100] |
Gisinger C, Eineder M, Brcic R, et al. First Experiences with Active C-Band Radar Reflectors and Sentinel-1[C]//IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2020.
|
| [101] |
Eineder M, Gisinger C, Brcic R, et al. Long Term Geodetic Monitoring Using Active C-Band Radar Transponders and Sentinel-1-First Results[C]// FRINGE 2021, Nice, France, 2021.
|
| [102] |
Yang M, Dheenathayalan P, Chang L, et al. High-Precision 3D Geolocation of Persistent Scatterers with One Single-Epoch GCP and LiDAR DSM Data[C]//Living Planet Symposium, Prague, Czech Republic, 2016.
|
| [103] |
Dheenathayalan P, Cuenca M, Hanssen R. Different Approaches for PSI Target Characterization For Monitoring Urban Infrastructure[C]//FRINGE 2011, Frascati, Italy, 2011.
|
| [104] |
Wang R, Yang T, Yang M, et al. A Safety Analysis of Elevated Highways in Shanghai Linked to Dynamic Load Using Long-term Time-series of InSAR Stacks[J]. Remote Sensing Letters, 2019, 10(12): 1133-1142. doi: 10.1080/2150704X.2019.1648903
|
| [105] |
Wang Y, Zhu X X, Zeisl B, et al. Fusing Meter-Resolution 4-D InSAR Point Clouds and Optical Images for Semantic Urban Infrastructure Monitoring[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(1): 14-26. doi: 10.1109/TGRS.2016.2554563
|
| [106] |
Wu Z, Ma P, Zheng Y, et al. Automatic Detection and Classification of Land Subsidence in Deltaic Metropolitan Areas Using Distributed Scatterer InSAR and Oriented R-CNN[J]. Remote Sensing of Environment, 2023, 290: 113545. doi: 10.1016/j.rse.2023.113545
|
| [107] |
Chang L, Sakpal N, Elberink S, et al. Railway Infrastructure Classification and Instability Identification Using Sentinel-1 SAR and Laser Scanning Data[J]. Sensors, 2020, 20(24): 7108. doi: 10.3390/s20247108
|
| [108] |
Schunert A, Soergel U. Assignment of Persistent Scatterers to Buildings[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(6): 3116-3127. doi: 10.1109/TGRS.2015.2511583
|
| [109] |
Sun Y, Montazeri S, Wang Y, et al. Automatic Registration of a Single SAR Image and GIS Building Footprints in a Large-Scale Urban Area[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170: 1-14. doi: 10.1016/j.isprsjprs.2020.09.016
|
| [110] |
Yang M S, López-Dekker P, Dheenathayalan P, et al. Linking Persistent Scatterers to the Built Environment Using Ray Tracing on Urban Models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(8): 5764-5776. doi: 10.1109/TGRS.2019.2901904
|
| [111] |
Solano-Rojas D, Wdowinski S, Cabral-Cano E, et al. Detecting Differential Ground Displacements of Civil Structures in Fast-Subsiding Metropolises with Interferometric SAR and Band-Pass Filtering[J]. Scientific Reports, 2020, 10: 15460. doi: 10.1038/s41598-020-72293-z
|
| [112] |
Lu P, Casagli N, Catani F, et al. Persistent Scatterers Interferometry Hotspot and Cluster Analysis (PSI-HCA) for Detection of Extremely Slow-moving Landslides[J]. International Journal of Remote Sensing, 2012, 33(2): 466-489. doi: 10.1080/01431161.2010.536185
|
| [113] |
Festa D, Bonano M, Casagli N, et al. Nation-Wide Mapping and Classification of Ground Deformation Phenomena Through the Spatial Clustering of P-SBAS InSAR Measurements: Italy Case Study[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 189: 1-22. doi: 10.1016/j.isprsjprs.2022.04.022
|
| [114] |
Schneider P J, Soergel U. Clustering Persistent Scatterer Points Based on a Hybrid Distance Metric[C]//German Conference on Pattern Recognition, Berlin, Germany, 2021.
|
| [115] |
Li Z, Zhao R, Hu J, et al. InSAR Analysis of Surface Deformation over Permafrost to Estimate Active Layer Thickness Based on One-Dimensional Heat Transfer Model of Soils[J]. Scientific Reports, 2015, 5: 15542. doi: 10.1038/srep15542
|
| [116] |
Zhu M, Wan X, Fei B, et al. Detection of Building and Infrastructure Instabilities by Automatic Spatiotemporal Analysis of Satellite SAR Interferometry Measurements[J]. Remote Sensing, 2018, 10(11): 1816. doi: 10.3390/rs10111816
|
| [117] |
Chang L, Hanssen R F. A Probabilistic Approach for InSAR Time-Series Postprocessing[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(1): 421-430. doi: 10.1109/TGRS.2015.2459037
|
| [118] |
Berti M, Corsini A, Franceschini S, et al. Automated Classification of Persistent Scatterers Interferometry Time Series[J]. Natural Hazards and Earth System Sciences, 2013, 13(8): 1945-1958. doi: 10.5194/nhess-13-1945-2013
|
| [119] |
Anantrasirichai N, Biggs J, Albino F, et al. A Deep Learning Approach to Detecting Volcano Deformation from Satellite Imagery Using Synthetic Datasets[J]. Remote Sensing of Environment, 2019, 230: 111179. doi: 10.1016/j.rse.2019.04.032
|
| [120] |
Rouet-Leduc B, Jolivet R, Dalaison M, et al. Autonomous Extraction of Millimeter-Scale Deformation in InSAR Time Series Using Deep Learning[J]. Nature Communications, 2021, 12(1): 6480. doi: 10.1038/s41467-021-26254-3
|
| [121] |
Liu Y, Yao X, Gu Z, et al. Study of the Automatic Recognition of Landslides by Using InSAR Images and the Improved Mask R-CNN Model in the Eastern Tibet Plateau[J]. Remote Sensing, 2022, 14(14): 3362. doi: 10.3390/rs14143362
|
| [122] |
李振洪, 宋闯, 余琛, 等. 卫星雷达遥感在滑坡灾害探测和监测中的应用: 挑战与对策[J]. 武汉大学学报(信息科学版), 2019, 44(7): 967-979. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907003.htm
Li Zhenhong, Song Chuang, Yu Chen, et al. Application of Satellite Radar Remote Sensing to Landslide Detection and Monitoring: Challenges and Solutions[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 967-979. https://www.cnki.com.cn/Article/CJFDTOTAL-WHCH201907003.htm
|
| [123] |
Kulshrestha A, Chang L, Stein A. Use of LSTM for Sinkhole-Related Anomaly Detection and Classification of InSAR Deformation Time Series[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15: 4559-4570. doi: 10.1109/JSTARS.2022.3180994
|
| [124] |
Lattari F, Rucci A, Matteucci M. A Deep Learning Approach for Change Points Detection in InSAR Time Series[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16.
|
| [125] |
Lee J S, Pottier E. Polarimetric Radar Imaging: From Basics to Applications[M]. London, New York: CRC Press, 2009.
|
| [126] |
Krogager E. New Decomposition of the Radar Target Scattering Matrix[J]. Electronics Letters, 2002, 26(18): 1525-1527.
|
| [127] |
Cameron W L, Youssef N N, Leung L K. Simulated Polarimetric Signatures of Primitive Geometrical Shapes[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(3): 793-803. doi: 10.1109/36.499784
|
| [128] |
Touzi R, Charbonneau F. Characterization of Target Symmetric Scattering Using Polarimetric SARs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2507-2516. doi: 10.1109/TGRS.2002.805070
|
| [129] |
Huynen J R. Phenomenological Theory of Radar Targets[D]. Delft : Delft University of Technology, 1970.
|
| [130] |
Holm W, Barnes R. On Radar Polarization Mixed Target State Decomposition Techniques[C]//IEEE National Radar Conference, Ann Arbor, USA, 2002.
|
| [131] |
Freeman A, Durden S L. A Three-Component Scattering Model for Polarimetric SAR Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973. doi: 10.1109/36.673687
|
| [132] |
Van Zyl J J. Unsupervised Classification of Scattering Behavior Using Radar Polarimetry Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1989, 27(1): 36-45. doi: 10.1109/36.20273
|
| [133] |
Dong Y, Forster B, Ticehurst C. Radar Backscatter Analysis for Urban Environments[J]. International Journal of Remote Sensing, 2010, 18(6): 1351-1364.
|
| [134] |
Ketelaar V, Hanssen R. Separation of Different Deformation Regimes Using PS-InSAR Data[C]// FRINGE 2003, Frascati, Italy, 2003.
|
| [135] |
Dheenathayalan P, Hanssen R. Target Characterization and Interpretation of Deformation Using Persistent Scatterer Interferometry and Polarimetry[C]//The 5th International Workshop on Science and Applications of SAR Polarimetry and Polarimetric Interferometry, Frascati, Italy, 2011.
|
| [136] |
Chang L, Kulshrestha A, Zhang B, et al. Extraction and Analysis of Radar Scatterer Attributes for PAZ SAR by Combining Time Series InSAR, PolSAR, and Land Use Measurements[J]. Remote Sensing, 2023, 15(6): 1571. doi: 10.3390/rs15061571
|
| [137] |
吴涛, 王超, 张红, 等. 基于图像特征的星载SAR图像模拟研究[J]. 遥感学报, 2007, 11(2): 214-220. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200702010.htm
Wu Tao, Wang Chao, Zhang Hong, et al. Space-borne SAR Image Simulation Based on Image Characteristics[J]. Journal of Remote Sensing, 2007, 11(2): 214-220. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200702010.htm
|
| [138] |
温晓阳, 张红, 王超. 地震损毁建筑物的高分辨率SAR图像模拟与分析[J]. 遥感学报, 2009, 13(1): 169-176. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200901036.htm
Wen Xiaoyang, Zhang Hong, Wang Chao. The High Resolution SAR Image Simulation and Analysis of the Damaged Building in Earthquake[J]. Journal of Remote Sensing, 2009, 13(1): 169-176. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200901036.htm
|
| [139] |
王国军, 邵芸, 张风丽. 城市建筑物SAR图像模拟综述[J]. 遥感信息, 2012, 27(4): 116-122. doi: 10.3969/j.issn.1000-3177.2012.04.021
Wang Guojun, Shao Yun, Zhang Fengli. Review of SAR Image Simulation for Urban Buildings[J]. Remote Sensing Information, 2012, 27(4): 116-122. doi: 10.3969/j.issn.1000-3177.2012.04.021
|
| [140] |
Franceschetti G, Migliaccio M, Riccio D, et al. SARAS: A Synthetic Aperture Radar (SAR) Raw Signal Simulator[J]. IEEE Transactions on Geo⁃science and Remote Sensing, 1992, 30(1): 110-123. doi: 10.1109/36.124221
|
| [141] |
Franceschetti G, Migliaccio M, Riccio D. On Ocean SAR Raw Signal Simulation[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(1): 84-100. doi: 10.1109/36.655320
|
| [142] |
Di Martino G, Iodice A, Poreh D, et al. Pol-SARAS: A Fully Polarimetric SAR Raw Signal Simulator for Extended Soil Surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2233-2247. doi: 10.1109/TGRS.2017.2777606
|
| [143] |
Huang Y H, Seguin G, Sultan N. Multi-frequency and Multi-polarization SAR System Analysis with Simulation Software Developed at CSA[C]//IEEE International Geoscience and Remote Sensing Symposium, Singapore, 2002.
|
| [144] |
Andersh D, Moore J, Kosanovich S, et al. Xpatch 4: The Next Generation in High Frequency Electromagnetic Modeling and Simulation Software[C]//IEEE International Radar Conference, Alexandria, USA, 2002.
|
| [145] |
Margarit G, Mallorqui J J, Rius J M, et al. On the Usage of GRECOSAR, An Orbital Polarimetric SAR Simulator of Complex Targets, to Vessel Classification Studies[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(12): 3517-3526. doi: 10.1109/TGRS.2006.881120
|
| [146] |
Hammer H, Schulz K. SAR-Simulation of Large Urban Scenes Using an Extended Ray Tracing Approach[C]//2011 Joint Urban Remote Sensing Event. Munich, Germany, 2011.
|
| [147] |
Balz T, Stilla U. Hybrid GPU-based Single- and Double-Bounce SAR Simulation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(10): 3519-3529. doi: 10.1109/TGRS.2009.2022326
|
| [148] |
Auer S, Hinz S, Bamler R. Ray-Tracing Simulation Techniques for Understanding High-Resolution SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(3): 1445-1456. doi: 10.1109/TGRS.2009.2029339
|
| [149] |
Hazlett M, Andersh D J, Lee S W, et al. XPATCH: A High-Frequency Electromagnetic Scattering Prediction Code Using Shooting and Bouncing Rays[J]. The International Society for Optical Engineering, 1995, 2469: 266-275.
|
| [150] |
Castelloe M W, Munson D C. 3-D SAR Imaging via High-Resolution Spectral Estimation Methods: Experiments with XPATCH[C]//International Conference on Image Processing, Washington, USA, 1997.
|
| [151] |
Bhalla R, Lin L, Andersh D. A Fast Algorithm for 3D SAR Simulation of Target and Terrain Using Xpatch[C]//IEEE International Radar Conference, Arlington, USA, 2005.
|
| [152] |
Auer S. 3D Synthetic Aperture Radar Simulation for Interpreting Complex Urban Reflection Scenarios[D]. München: Technische Universität München, 2011.
|
| [153] |
Auer S, Gernhardt S, Bamler R. Ghost Persistent Scatterers Related to Multiple Signal Reflections[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(5): 919-923. doi: 10.1109/LGRS.2011.2134066
|
| [154] |
Auer S, Gernhardt S. Linear Signatures in Urban SAR Images—Partly Misinterpreted?[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(10): 1762-1766. doi: 10.1109/LGRS.2014.2308353
|
| [155] |
Auer S, Gisinger C, Tao J. Characterization of Facade Regularities in High-Resolution SAR Images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(5): 2727-2737. doi: 10.1109/TGRS.2014.2364076
|
| [156] |
张月婷, 仇晓兰, 丁赤飚, 等. 高分辨率SAR图像桥梁目标仿真与特性分析[J]. 雷达学报, 2015, 4(1): 78-83. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201501010.htm
Zhang Yueting, Qiu Xiaolan, Ding Chibiao, et al. The Simulation and Characteristics Analysis on High Resolution SAR Images of Bridges[J]. Journal of Radars, 2015, 4(1): 78-83. https://www.cnki.com.cn/Article/CJFDTOTAL-LDAX201501010.htm
|
| [157] |
赵婧文, 伍吉仓, 丁晓利. 基于RaySAR软件的上海外滩建筑群合成孔径雷达图像模拟[J]. 同济大学学报(自然科学版), 2018, 46(4): 557-564. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201804021.htm
Zhao Jingwen, Wu Jicang, Ding Xiaoli. Synthetic Aperture Radar Image Simulation of the Bund Building Group in Shanghai Based on RaySAR Software[J]. Journal of Tongji University (Natural Science), 2018, 46(4): 557-564. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201804021.htm
|
| [158] |
Cheng R, Liang X, Zhang F, et al. Multipath Scattering of Typical Structures in Urban Areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(1): 342-351. doi: 10.1109/TGRS.2018.2854660
|
| [159] |
Lei S, Qiu X, Zhang Y, et al. Analysis of the Multipath Scattering Effects in High-Resolution SAR Images[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(4): 616-620. doi: 10.1109/LGRS.2019.2930527
|
| [160] |
Yang M S. From Radar to Reality. Associating Persistent Scatterers to Corresponding Objects[D]. Delft: Delft University of Technology, 2020.
|
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