Elevation Extraction and Deformation Monitoring by Multitemporal InSAR of Lupu Bridge in Shanghai

Monitoring, assessing, and understanding the structural health of large infrastructures, such as buildings, bridges, dams, tunnels, and highways, is important for urban development and management, as the gradual deterioration of such structures may result in catastrophic structural failure leading to high personal and economic losses. With a higher spatial resolution and a shorter revisit period, interferometric synthetic aperture radar (InSAR) plays an increasing role in the deformation monitoring and height extraction of structures. As a focal point of the InSAR data processing chain, phase unwrapping has a direct impact on the accuracy of the results. In complex urban areas, large elevation differences between the top and bottom parts of a large structure combined with a long interferometric baseline can result in a serious phase-wrapping problem. Here, with no accurate digital surface model (DSM) available, we handle the large phase gradients of arcs in multitemporal InSAR processing using a long–short baseline iteration method. Specifically, groups of interferometric pairs with short baselines are processed to obtain the rough initial elevation estimations of the persistent scatterers (PSs). The baseline threshold is then loosened in subsequent iterations to improve the accuracy of the elevation estimates step by step. The LLL lattice reduction algorithm (by Lenstra, Lenstra, and Lovasz) is applied in the InSAR phase unwrapping process to rapidly reduce the search radius, compress the search space, and improve the success rate in resolving the phase ambiguities. Once the elevations of the selected PSs are determined, they are used in the following two-dimensional phase regression involving both elevations and deformations. A case study of Lupu Bridge in Shanghai is carried out for the algorithm’s verification. The estimated PS elevations agree well (within 1 m) with the official Lupu Bridge model data, while the PS deformation time series confirms that the bridge exhibits some symmetric progressive deformation, at 4–7 mm per year on both arches and 4–9 mm per year on the bridge deck during the SAR image acquisition period.

[1]  Sina Montazeri,et al.  Three-Dimensional Deformation Monitoring of Urban Infrastructure by Tomographic SAR Using Multitrack TerraSAR-X Data Stacks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Daniele Perissin,et al.  Potential of satellite InSAR techniques for monitoring of bridge deformations , 2015, 2015 Joint Urban Remote Sensing Event (JURSE).

[3]  Kanika Goel,et al.  Thermal dilation monitoring of complex urban infrastructure using high resolution SAR data , 2014, 2014 IEEE Geoscience and Remote Sensing Symposium.

[4]  Xiaoxiang Zhu,et al.  Facade Reconstruction Using Multiview Spaceborne TomoSAR Point Clouds , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Alessandro Parizzi,et al.  Wide area Persistent Scatterer Interferometry: Current developments, algorithms and examples , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

[6]  Gianfranco Fornaro,et al.  Bridge Thermal Dilation Monitoring With Millimeter Sensitivity via Multidimensional SAR Imaging , 2013, IEEE Geoscience and Remote Sensing Letters.

[7]  Gianfranco Fornaro,et al.  Extension of 4-D SAR Imaging to the Monitoring of Thermally Dilating Scatterers , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[8]  S. Gernhardt,et al.  Deformation monitoring of single buildings using meter-resolution SAR data in PSI , 2012 .

[9]  Xiao Xiang Zhu,et al.  Very High Resolution Tomographic SAR Inversion for Urban Infrastructure Monitoring — A Sparse and Nonlinear Tour , 2011 .

[10]  Michele Crosetto,et al.  The Thermal Expansion Component of Persistent Scatterer Interferometry Observations , 2011, IEEE Geoscience and Remote Sensing Letters.

[11]  Michele Crosetto,et al.  A new product from persistent scatterer interferometry: The thermal dilation maps , 2011, 2011 Joint Urban Remote Sensing Event.

[12]  Andrew Hooper,et al.  Phase unwrapping in three dimensions with application to InSAR time series. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[13]  B. Kampes Radar Interferometry: Persistent Scatterer Technique , 2006 .

[14]  José M. Bioucas-Dias,et al.  Phase Unwrapping via Graph Cuts , 2005, IEEE Transactions on Image Processing.

[15]  Munther A. Gdeisat,et al.  Fast two-dimensional phase-unwrapping algorithm based on sorting by reliability following a noncontinuous path. , 2002, Applied optics.

[16]  Carlo Atzeni,et al.  Terrain mapping by ground-based interferometric radar , 2001, IEEE Trans. Geosci. Remote. Sens..

[17]  Paul Fieguth,et al.  Probabilistic cost functions for network flow phase unwrapping , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[18]  David G. Long,et al.  Multi-baseline interferometric SAR for iterative height estimation , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[19]  F. Rocca,et al.  Permanent scatterers in SAR interferometry , 1999, IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293).

[20]  Don Coppersmith,et al.  Small Solutions to Polynomial Equations, and Low Exponent RSA Vulnerabilities , 1997, Journal of Cryptology.

[21]  T. J. Flynn,et al.  Consistent 2-D phase unwrapping guided by a quality map , 1996, IGARSS '96. 1996 International Geoscience and Remote Sensing Symposium.

[22]  C. Werner,et al.  Satellite radar interferometry: Two-dimensional phase unwrapping , 1988 .

[23]  László Lovász,et al.  Factoring polynomials with rational coefficients , 1982 .

[24]  Daniele Perissin,et al.  Bridge Displacements Monitoring Using Space-Borne X-Band SAR Interferometry , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Daniele Perissin,et al.  Social Care Information Systems and Technologies Infrastructure Non-Linear Deformation Monitoring Via Satellite Radar Interferometry , 2014 .

[26]  S. Gernhardt High Precision 3D Localization and Motion Analysis of Persistent Scatterers using Meter-Resolution Radar Satellite Data , 2012 .

[27]  Liu Jingnan GNSS Ambiguity Resolution Using the Lattice Theory , 2012 .

[28]  Chen Li,et al.  A Residue-Pairing Algorithm for InSAR Phase Unwrapping , 2009 .

[29]  Curtis W. Chen Statistical-cost network-flow approaches to two-dimensional phase unwrapping for radar interferometry , 2001 .

[30]  Wei Xu,et al.  A region-growing algorithm for InSAR phase unwrapping , 1999, IEEE Trans. Geosci. Remote. Sens..

[31]  E. Kaltofen,et al.  Explicit Construction of the Hilbert Class Fields of Imaginary Quadratic Fields by Integer Lattice Reduction , 1991 .

[32]  Michael Pohst,et al.  On Reduction Algorithms in Non Linear Integer Mathematical Programming , 1984 .

[33]  Jeffrey C. Lagarias,et al.  Knapsack Public Key Cryptosystems and Diophantine Approximation , 1983, CRYPTO.