High-resolution High-dimensional Imaging of Urban Building Based on GaoFen-3 SAR Data

被引:0
|
作者
Bi H. [1 ]
Jin S. [1 ]
Wang X. [2 ]
Li Y. [1 ]
Han B. [3 ]
Hong W. [3 ]
机构
[1] College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing
[2] School of Computer Science and Technology, Nanjing Tech University, Nanjing
[3] Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing
基金
中国国家自然科学基金;
关键词
Differential Synthetic Aperture Radar Tomography (D-TomoSAR); Four-Dimensional (4-D) imaging; GaoFen-3 (GF-3); Synthetic Aperture Radar Tomography (TomoSAR); Three-Dimensional (3-D) imaging;
D O I
10.12000/JR21113
中图分类号
学科分类号
摘要
Conventional Synthetic Aperture Radar (SAR) can only obtain two-dimensional (2-D) azimuth-range images without accurately reflecting the three-Dimensional (3-D) scattering structure information of the targets. However, SAR Tomography (TomoSAR) is a multi-baseline interferometric measurement mode that extends the synthetic aperture principle into the elevation direction, making it possible to recover the true height of the target, thereby achieving 3-D imaging. Moreover, Differential SAR Tomography (D-TomoSAR) extends the synthetic aperture principle into the elevation and time directions simultaneously. Thus, it can obtain the target 3-D scattering structure along with the deformation speed of the observed target. GaoFen-3 (GF-3) is the first C-band multi-polarization 1 m resolution SAR satellite of China. It has several advantages, such as high-resolution, large swath width, and multiple imaging modes, which are crucial to the development of a high-resolution earth observation technology for China. Presently, GF-3 data are mainly used in the image processing field, such as target identification. However, the phase information of the SAR images is not yet fully utilized. Moreover, because of the high-dimensional imaging ability that was overlooked at the beginning of designing the system, existing SAR images acquired by GF-3 have spatial and temporal de-coherence problems. Thus, it is difficult to use the images in further interference series processing. To solve the above problems, this study achieved 3-D and four-Dimensional (4-D) imaging of buildings around Yanqi Lake, in Beijing, based on the data of seven SAR complex images. We obtained the 3-D scattering structure information of buildings and achieved millimeter-level high-precision monitoring of building deformation. The preliminary experimental results demonstrate the application potential of GF-3 SAR data and provide a technical support for the subsequent further application of the GF-3 SAR satellite in urban sensing and monitoring. © 2022 Institute of Electronics Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码:40 / 51
页数:11
相关论文
共 46 条
  • [11] LOMBARDINI F., Differential tomography: A new framework for SAR interferometry[C], IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium, pp. 1206-1208, (2003)
  • [12] FORNARO G, PAUCIULLO A, SERAFINO F., Multipass SAR processing for urbanized areas imaging and deformation monitoring at small and large scales[C], 2007 Urban Remote Sensing Joint Event, pp. 1-7, (2007)
  • [13] FORNARO G, REALE D, SERAFINO F., 4D SAR focusing: A tool for improved imaging and monitoring of urban areas[C], IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium, (2008)
  • [14] ZHU Xiaoxiang, ADAM N, BAMLER R., Space-borne high resolution tomographic interferometry[C], 2009 IEEE International Geoscience and Remote Sensing Symposium, (2009)
  • [15] FORNARO G, SERAFINO F, REALE D., 4-D SAR imaging: The case study of Rome[J], IEEE Geoscience and Remote Sensing Letters, 7, 2, pp. 236-240, (2010)
  • [16] Xiaoxiang ZHU, BAMLER R., Let’s do the time warp: Multicomponent nonlinear motion estimation in differential SAR tomography[J], IEEE Geoscience and Remote Sensing Letters, 8, 4, pp. 735-739, (2011)
  • [17] SIDDIQUE M A, HAJNSEK I, AERSOSPACE G, Et al., Investigating the combined use of differential SAR tomography and PSI for spatio-temporal inversion[C], 2015 Joint Urban Remote Sensing Event (JURSE), pp. 1-4, (2015)
  • [18] WANG Zhigui, LIU Mei, Seasonal deformation and accelerated motion of infrastructure monitoring using a generalized differential SAR tomography[J], IEEE Geoscience and Remote Sensing Letters, 17, 4, pp. 626-630, (2020)
  • [19] CANDES E J., Compressive sampling[C], The International C o n g r e s s o f M a t h e m a t i c i a n s, 2, pp. 1433-1452
  • [20] DONOHO D L., Compressed sensing[J], IEEE Transactions on Information Theory, 52, 4, pp. 1289-1306, (2006)