Ramon F. Hanssen

Ambiguity resolution for permanent scatterer interferometry

In the permanent scatterer technique of synthetic aperture radar interferometry, there is a need for an efficient and reliable nonlinear parameter inversion algorithm that includes estimation of the phase cycle ambiguities. Present techniques make use of a direct search of the solution space, treating the observations as deterministic and equally weighted, and which do not yield an exact solution. Moreover, they do not describe the quality of the estimated parameters. Here, we use the integer least squares estimator, which has the highest probability of correct integer estimation for problems with a multivariate normal distribution. With this estimator, the propagated variance-covariance matrix of the estimated parameters can be obtained. We have adapted the LAMBDA method, part of an integer least squares estimator developed for the ambiguity resolution of carrier phase observations in global positioning systems, to the problem of permanent scatterers. Key elements of the proposed method are the introduction of pseudo-observations to regularize the system of equations, decorrelation of the ambiguities for an efficient estimation, and the combination of a bootstrap estimator with an integer least squares search to obtain the final integer estimates. The performance of the proposed algorithm is demonstrated using simulated and real data.

ASAR ERS interferometric phase continuity

For ten years, a long history of data was acquired by the SAR sensors on the satellite ERS-1 and ERS-2 offering a wide range of interferometric applications. In 2002, the more advanced satellite ENVISAT was launched. The SAR on board on ENVISAT (ASAR) can continue the success of the remote sensing mission of the ERS satellites and preserve or even increase the value of the archived ERS data. The subject of this study is to demonstrate the continuity of the interferometric measurements by the combination of the SAR scene of the different sensors to interferograms (cross interferometry).

Evaluation of interpolation kernels for SAR interferometry

Interpolation is required in interferometric synthetic aperture radar (SAR) processing for coregistration of complex signals. Straightforward system theoretical considerations provide objective figures of merit for interpolators, such as interferometric decorrelation and phase noise. Theoretical and simulation results are given for nearest neighbor, piecewise linear, four- and six-point cubic convolution, and truncated sinc kernels.

Merging GPS and Atmospherically Corrected InSAR Data to Map 3-D Terrain Displacement Velocity

A method to derive accurate spatially dense maps of 3-D terrain displacement velocity is presented. It is based on the merging of terrain displacement velocities estimated by time series of interferometric synthetic aperture radar (InSAR) data acquired along ascending and descending orbits and repeated GPS measurements. The method uses selected persistent scatterers (PSs) and GPS measurements of the horizontal velocity. An important step of the proposed method is the mitigation of the impact of atmospheric phase delay in InSAR data. It is shown that accurate vertical velocities at PS locations can be retrieved if smooth horizontal velocity variations can be assumed. Furthermore, the mitigation of atmospheric effects reduces the spatial dispersion of vertical velocity estimates resulting in a more spatially regular 3-D velocity map. The proposed methodology is applied to the case study of Azores islands characterized by important tectonic phenomena.

Impact of DEM-Assisted Coregistration on High-Resolution SAR Interferometry

Image alignment is a crucial step in synthetic aperture radar (SAR) interferometry. Interferogram formation requires images to be coregistered with an accuracy of better than a few tenths of a resolution cell to avoid significant loss of phase coherence. In conventional interferometric precise coregistration methods for full-resolution SAR data, a 2-D polynomial of low degree is usually chosen as warp function, and the polynomial parameters are estimated through least squares fit from the shifts measured on image windows. In case of rough topography or long baselines, the polynomial approximation may become inaccurate, leading to local misregistrations. These effects increase with spatial resolution of the sensor. An improved elevation-assisted image-coregistration procedure can be adopted to provide better prediction of the offset vectors. This approach computes pixel by pixel the correspondence between master and slave acquisitions by using the orbital data and a reference digital elevation model (DEM). This paper aims to assess the performance of this procedure w.r.t. the “standard” one based on polynomial approximation. Analytical relationships and simulations are used to evaluate the improvement of the DEM-assisted procedure w.r.t. the polynomial approximation as well as the impact of the finite vertical accuracy of the DEM on the final coregistration precision for different resolutions and baselines. The two approaches are then evaluated experimentally by processing high-resolution SAR data provided by the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO/SkyMed) and TerraSAR-X missions, acquired over mountainous areas in Italy and Tanzania, respectively. Residual-range pixel offsets and interferometric coherence are used as quality figure.

Temporal Decorrelation in L-, C-, and X-band Satellite Radar Interferometry for Pasture on Drained Peat Soils

Temporal decorrelation is one of the main limitations of synthetic aperture radar (SAR) interferometry. For nonurban areas, its mechanism is very complex, as it is very dependent of vegetation types and their temporal dynamics, actual land use, soil types, and climatological circumstances. Yet, an a priori assessment and comprehension of the expected coherence levels of interferograms are required for designing new satellite missions (in terms of frequency, resolution, and repeat orbits), for choosing the optimal data sets for a specific application, and for feasibility studies for new interferometric applications. Although generic models for temporal decorrelation have been proposed, their parameters depend heavily on the land use in the area of interest. Here, we report the behavior of temporal decorrelation for a specific class of land use: pasture on drained peat soils. We use L-, C-, and X-band SAR observations from the Advanced Land Observation Satellite (ALOS), European Remote Sensing Satellite, Envisat, RADARSAT-2, and TerraSAR-X missions. We present a dedicated temporal decorrelation model using three parameters and demonstrate how coherent information can be retrieved as a function of frequency, repeat intervals, and coherence estimation window sizes. New satellites such as Sentinel-1 and ALOS-2, with shorter repeat intervals than their predecessors, would enhance the possibility to obtain a coherent signal over pasture.

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.

Comparison of Precipitable Water Vapor Observations by Spaceborne Radar Interferometry and Meteosat 6.7-μm Radiometry

Abstract Satellite radar interferometry (InSAR) can be applied to study vertically integrated atmospheric refractivity variations with a spatial resolution of 20 m and an accuracy of 2 mm, irrespective of cloud cover or solar illumination. The data are derived from the difference between the radar signal delay variations within the imaged area during two acquisitions with a temporal separation of one or more days. Hence, they reflect the superposition of the refractivity distribution during these two acquisitions. On short spatial scales, integrated refractivity variations are dominantly caused by spatial heterogeneities in the water vapor distribution. Validation of the radar interferometric results can be difficult, since conventional imaging radiometers do not provide quantitative measures for water vapor content over the entire tropospheric column and are lacking in spatial resolution. Moreover, comparable quantitative data such as signal delay observed by Global Positioning System (GPS) receivers are...

Geodetic SAR Tomography

In this paper, we propose a framework referred to as “geodetic synthetic aperture radar (SAR) tomography” that fuses the SAR imaging geodesy and tomographic SAR inversion (TomoSAR) approaches to obtain absolute 3-D positions of a large amount of natural scatterers. The methodology is applied on four very high resolution TerraSAR-X spotlight image stacks acquired over the city of Berlin. Since all the TomoSAR estimates are relative to the same reference point object whose absolute 3-D positions are retrieved by means of stereo SAR, the point clouds reconstructed using data acquired from different viewing angles can be geodetically fused. To assess the accuracy of the position estimates, the resulting absolute shadow-free 3-D TomoSAR point clouds are compared with a digital surface model obtained by airborne LiDAR. It is demonstrated that an absolute positioning accuracy of around 20 cm and a meter-order relative positioning accuracy can be achieved by the proposed framework using TerraSAR-X data.

Improved SAR Image Coregistration Using Pixel-Offset Series

Synthetic aperture radar (SAR) image coregistration is a key procedure before interferometric SAR (InSAR) time-series analysis can be started. However, many geophysical data sets suffer from severe decorrelation problems due to a variety of reasons, making precise coregistration a nontrivial task. Here, we present a new strategy that uses a pixel-offset series of detected subimage patches dominated by point-like targets (PTs) to improve SAR image coregistrations. First, all potentially coherent image pairs are coregistered in a conventional way. In this step, we propose a coregistration quality index for each image to rank its relative “significance” within the data set and to select a reference image for the SAR data set. Then, a pixel-offset series of detected PTs is made from amplitude maps to improve the geometrical mapping functions. Finally, all images are resampled depending on the pixel offsets calculated from the updated geometrical mapping functions. We used images from a rural region near the North Anatolian Fault in eastern Turkey to test the proposed method, and clear coregistration improvements were found based on amplitude stability. This enhanced the fact that the coregistration strategy should therefore lead to improved InSAR time-series analysis results.