Multi-temporal interferometric synthetic aperture radar (MT-InSAR) can be applied to monitor the structural health of infrastructure such as railways, bridges, and highways. However, for the successful interpretation of the observed deformation within a structure, or between structures, it is imperative to associate a radar scatterer unambiguously with an actual physical object. Unfortunately, the limited positioning accuracy of the radar scatterers hampers this attribution, which limits the applicability of MT-InSAR. In this study, we propose an approach for health monitoring of railway system combining MT-InSAR and LiDAR (laser scanning) data. An amplitude-augmented interferometric processing approach is applied to extract continuously coherent scatterers (CCS) and temporary coherent scatterers (TCS), and estimate the parameters of interest. Based on the 3D confidence ellipsoid and a decorrelation transformation, all radar scatterers are linked to points in the point cloud and their coordinates are corrected as well. Additionally, several quality metrics defined using both the covariance matrix and the radar geometry are introduced to evaluate the results. Experimental results show that most radar scatterers match well with laser points and that LiDAR data are valuable as auxiliary data to classify the radar scatterers.

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is used for many applications in earth observation. Most MT-InSAR methods select scatterers with high coherence throughout the entire time series. However, as time series lengthen, inevitable changes in surface scattering lead to decorrelation, which systematically decreases the number of coherent scatterers. Here, we propose a novel method to detect and process temporary coherent scatterers (TCS) by subsequently analyzing the amplitude and the interferometric phase. Two hypothesis tests are developed for amplitude analysis in order to identify the moments of appearing and/or disappearing coherent scatterers. Based on the amplitude analysis, the parameters of interest are then estimated using the interferometric phase. An optimized adaptive temporal subset approach is proposed to improve the precision of the estimated parameters. If the scatterers are not evenly distributed over the area, a secondary (support) network is designed to improve the spatial point distribution. The main advantage of this method is the reliable extraction of a subset of time series without using any contextual information. Experimental results show that the TCSs significantly increase the number of observations for displacement monitoring and improve the change detection capability in urban construction areas.

In this letter, the permanent scatterer interferometric synthetic aperture radar (PS-InSAR) method is applied to extract ground subsidence using X-band TerraSAR-X images. A zone of 1 km width on both sides of the Shanghai maglev was selected, and the ground subsidence rates were obtained on all permanent scatters or highly coherent targets. The results show that permanent scatters are distributed on the rail of the maglev, and the subsidence rates of these PS points are mostly less than 3 mm/a. A KS-test is formulated to judge the agreements between the subsidence results obtained by PS-InSAR and those obtained by spirit leveling, which indicated that PS-InSAR results are acceptable statistically. In addition, the PS-InSAR results show some ground subsiding troughs near the maglev track, with subsidence rates of more than 10 mm/a.

In airborne array interferometric synthetic aperture radar (Array-InSAR) tomography, the measurements acquired by conventional uniform sampling array are always restricted by the number of physical baseline elements and the size of baseline aperture. It is desirable to capture new acquisitions and enlarge the aperture with virtual signal processing instead of actually adding array baselines. For this motivation, we utilize the disparity of a pair of coprime sampling subarrays to enlarge the baseline aperture and construct new observations virtually. The generation of virtual measurements is equal to estimating cross-correlation matrices in real SAR data. Due to the spatial target variation, we adopted an adaptive filtering method to estimate the cross-correlation matrix. We call the abovementioned processing of generating virtual measurements as a coprime sensing technique . The newly generated virtual measurements have more degrees of freedom, a larger baseline aperture, and a higher signal-to-noise ratio (SNR) than the physical measurements. These advantages offer the possibility to obtain competitive 3-D imaging results without increasing the hardware cost of the Array-InSAR. We demonstrate the effectiveness of the proposed method by the coprime acquisitions selected from AIRCAS Array-InSAR data.

In the multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique, a deformation map is obtained through the integration of arc solutions. The reliability of the arc solutions directly influences the accuracy of the final deformation results. In this study, the influence of phase ambiguity on the arc solutions is analysed, and two indicators are proposed to identify the arcs with phase ambiguity. Precise variance–covariance estimation is implemented using the high-quality arcs, which are selected using four proposed indicators. The lambda method is used to perform phase unwrapping, and the low-quality arcs are removed according to the conditions of the indicators. In addition, the network is reconstructed based on a network search strategy to link as many coherent points as possible. Our improved MT-InSAR method is tested by processing 35 repeat COSMO-SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) SAR images covering Shanghai.