Abstract
Point-like targets are useful in providing surface deformation with the time series of synthetic aperture radar (SAR) images using the multi-temporal interferometric synthetic aperture radar (MTInSAR) methodology. However, the spatial density of point-like targets is low, especially in non-urban areas. In this paper, a hierarchical MTInSAR method is proposed to increase the spatial density of deformation measurements by tracking both the point-like targets and the distributed targets with the temporal steadiness of radar backscattering. To efficiently reduce error propagation, the deformation rates on point-like targets with lower amplitude dispersion index values are first estimated using a least squared estimator and a region growing method. Afterwards, the distributed targets are identified using the amplitude dispersion index and a Pearson correlation coefficient through a multi-level processing strategy. Meanwhile, the deformation rates on distributed targets are estimated during the multi-level processing. The proposed MTInSAR method has been tested for subsidence detection over a suburban area located in Tianjin, China using 40 high-resolution TerraSAR-X images acquired between 2009 and 2010, and validated using the ground-based leveling measurements. The experiment results indicate that the spatial density of deformation measurements can be increased by about 250% and that subsidence accuracy can reach to the millimeter level by using the hierarchical MTInSAR method.
Highlights
The statistical analysis indicates that the mean and standard deviation (SD) of all the amplitude dispersion index (ADI) values are 0.65 and 0.19, respectively, and an ADI value belongs to [0.08, 1.22] at a confidence level of 99.7%
As the rigorous quality control was performed during the solution, the 91,601 pixels were determined as the valid pixels from all the 137,698 pixels in Group 0
It should be noted that all the subsidence results are referenced to CR5, and the spatial density of the deformation measurements is 753 km−2
Summary
The multi-temporal interferometric synthetic aperture radar (MTInSAR) methodology detects point-like targets (PTs) from SAR image time series to provide surface deformation information [1,2,3,4,5,6,7]. To increase spatial resolution and the coverage of deformation information, this paper presents an improved MTInSAR method by tracking both PTs and DTs with the temporal steadiness of radar reflectivity. As the Pearson correlation coefficient (PCC) works well for measuring the data correlation in time and space [20], we use the thresholding of both ADI and PCC for selecting the useful pixels corresponding to the PTs or DTs. To control error propagation, a hierarchical analysis strategy is applied to extract deformation rates at the useful pixels.
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