Ling Chang

Fast Statistically Homogeneous Pixel Selection for Covariance Matrix Estimation for Multitemporal InSAR

Multitemporal interferometric synthetic aperture radar (InSAR) is increasingly being used for Earth observations. Inaccurate estimation of the covariance matrix is considered to be the most important source of error in such applications. Previous studies, namely, DeSpecKS and its variants, have demonstrated their advantages in improving the estimation accuracy for distributed targets by means of statistically homogeneous pixels (SHPs). However, these methods may be unreliable for small sample sizes and sensitive to data stacks showing large time spacing due to the variability of the temporal sample. Moreover, these methods are computationally intensive. In this paper, a new algorithm named fast SHP selection (FaSHPS) is proposed to solve both problems. FaSHPS explores the confidence interval for each pixel by invoking the central limit theorem and then selects SHPs using this interval. Based on identified SHPs, two estimators with respect to the despeckling and the bias mitigation of the sample coherence are proposed to refine the elements of the InSAR covariance matrix. A series of qualitative and quantitative evaluations are presented to demonstrate the effectiveness of our method.

A Probabilistic Approach for InSAR Time-Series Postprocessing

Monitoring the kinematic behavior of enormous amounts of points and objects anywhere on Earth is now feasible on a weekly basis using radar interferometry from Earth-orbiting satellites. An increasing number of satellite missions are capable of delivering data that can be used to monitor geophysical processes, mining and construction activities, public infrastructure, or even individual buildings. The parameters estimated from these data are used to better understand various natural hazards, improve public safety, or enhance asset management activities. Yet, the mathematical estimation of kinematic parameters from interferometric data is an ill-posed problem as there is no unique solution, and small changes in the data may lead to significantly different parameter estimates. This problem results in multiple possible outcomes given the same data, hampering public acceptance, particularly in critical conditions. Here, we propose a method to address this problem in a probabilistic way, which is based on multiple hypotheses testing. We demonstrate that it is possible to systematically evaluate competing kinematic models in order to find an optimal model and to assign likelihoods to the results. Using the B-method of testing, a numerically efficient implementation is achieved, which is able to evaluate hundreds of competing models per point. Our approach will not solve the nonuniqueness problem of interferometric synthetic aperture radar (InSAR), but it will allow users to critically evaluate (conflicting) results, avoid overinterpretation, and thereby consolidate InSAR as a geodetic technique.

Detection of permafrost sensitivity of the Qinghai–Tibet railway using satellite radar interferometry

Climate change and human involvement are changing the dynamics of permafrost environments, with potential impact on the safety and stability of infrastructure. The Qinghai–Tibet Railway (QTR) has been designed to withstand the dynamic permafrost conditions. Yet, in situ measurements of the track stability at elevations of about 5 km are scarce. Here we investigate whether satellite radar interferometry can be used to detect indications of permafrost-related instabilities over an 80 km segment at the highest part of the QTR. An analysis method using all available pixels over the track is developed and implemented. We find inhomogeneous deformation along the track, with vertical rates of up to 10 mm year−1. We also find seasonal displacements over a range of 15 mm. We conclude that the satellite time series are able to detect variability with characteristics similar as expected from permafrost dynamics. While the signal cannot be unambiguously attributed to permafrost, the approach demonstrates the value of continuous satellite observations for the operational safety of the QTR.

Structural health monitoring of railway transition zones using satellite radar data

Transition zones in railway tracks are locations with considerable changes in the rail-supporting structure. Typically, they are located near engineering structures, such as bridges, culverts and tunnels. In such locations, severe differential settlements often occur due to the different material properties and structure behavior. Without timely maintenance, the differential settlement may lead to the damage of track components and loss of passenger’s comfort. To ensure the safety of railway operations and reduce the maintenance costs, it is necessary to consecutively monitor the structural health condition of the transition zones in an economical manner and detect the changes at an early stage. However, using the current in situ monitoring of transition zones is hard to achieve this goal, because most in situ techniques (e.g., track-measuring coaches) are labor-consuming and usually not frequently performed (approximately twice a year in the Netherlands). To tackle the limitations of the in situ techniques, a Satellite Synthetic Aperture Radar (InSAR) system is presented in this paper, which provides a potential solution for a consecutive structural health monitoring of transition zones with bi-/tri-weekly data update and mm-level precision. To demonstrate the feasibility of the InSAR system for monitoring transition zones, a transition zone is tested. The results show that the differential settlement in the transition zone and the settlement rate can be observed and detected by the InSAR measurements. Moreover, the InSAR results are cross-validated against measurements obtained using a measuring coach and a Digital Image Correlation (DIC) device. The results of the three measuring techniques show a good correlation, which proves the applicability of InSAR for the structural health monitoring of transition zones in railway track.

Near real-time, semi-recursive, deformation monitoring of infrastructure using satellite radar interferometry

Conventional PSI technology is aimed towards estimating displacement time series of persistently coherent scatterers (PS) from a given set of radar acquisitions. Whenever the data from a new acquisition become available, the estimators for the parameters of interest will be computed by re-adjustment of the system of equations. This strategy of batch processing after a new acquisition is not optimal to identify changes in the behavior of single scatterer. For monitoring the structural health of buildings and civil infrastructure, there is a need for fast identification of anomalous behavior of scatterers, including the likelihood estimations of such detection results. Here we propose a general framework for the detection of anomalous behavior of (parts of) buildings and civil infrastructure by generating a sequential update of conventional interferograms, in combination with the parallel processing of the data using time series (PSI) interferometry. By estimating and analyzing the phase change per arc from each wrapped interferogram, abnormal changes can be detected fast and reliably. Our approach is demonstrated on a near-collapse of a building in Heerlen, the Netherlands, using Radarsat-2 data.

Functional model selection for InSAR time series

InSAR time series analysis involves the processing of extremely large datasets to estimate the relative movements of points on Earth. The estimated movements may reveal geophysical processes, or strain in anthropogenic structures. In parametric estimation methods, it is important to chose the optimal mathematical functional model relating the satellite observations to the kinematic parameters of interest. A standard approach is to parameterize the kinematic behavior, in first order, as a linear function of time, but it is unlikely that all objects behave in this purely linear way. Ideally, the kinematic parameterization should be optimized for each individual measurement point in the area of interest. In this work, following [1] we introduce a method to select the optimal functional model, with a minimum but sufficient number of free parameters using a probabilistic method based on multiple hypotheses testing.