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Title: Potential of TCPInSAR in monitoring linear infrastructure with a small dataset of SAR images : application of the Donghai Bridge, China
Authors: Zhang, L 
Sun, Q
Hu, J
Keywords: InSAR
Coherent point
Donghai Bridge
Deformation monitoring
Issue Date: 2018
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Applied sciences, Mar. 2018, v. 8, no. 3, 425 How to cite?
Journal: Applied sciences 
Abstract: Reliably monitoring deformation associated with linear infrastructures, such as long-span bridges, is vitally important to assess their structural health. In this paper, we attempt to employ satellite interferometric synthetic aperture radar (InSAR) to map the deformation of Donghai Bridge over a half of an annual cycle. The bridge, as the fourth longest cross-sea bridge in the world, located in the north of Hangzhou Bay, East China Sea where the featureless sea surface largely occupied the radar image raises challenges to accurately co-register the coherent points along the bridge. To tackle the issues due to co-registration and the limited number of synthetic aperture radar (SAR) images, we adopt the termed temporarily-coherent point (TCP) InSAR (TCPInSAR) technique to process the radar images. TCPs that are not necessarily coherent during the whole observation period can be identified within every two SAR acquisitions during the co-registration procedure based on the statistics of azimuth and range offsets. In the process, co-registration is performed only using the offsets of these TCPs, leading to improved interferometric phases and the local Delaunay triangulation is used to construct point pairs to reduce the atmospheric artifacts along the bridge. With the TCPInSAR method the deformation rate along the bridge is estimated with no need of phase unwrapping. The achieved result reveals that the Donghai Bridge suffered a line-of-sight (LOS) deformation rate up to 2.3 cm/year from January 2009 to July 2009 at the cable-stayed part, which is likely due to the thermal expansion of cables.
ISSN: 2076-3417
DOI: 10.3390/app8030425
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