Three-Dimensional Deformation Monitoring of Urban Infrastructure by Tomographic SAR Using Multitrack TerraSAR-X Data Stacks

Montazeri, Sina; Zhu, Xiao Xiang; Eineder, Michael; Bamler, Richard

doi:10.1109/tgrs.2016.2585741

Cited by 36 publications

(32 citation statements)

References 31 publications

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“…It estimates the 3D motion components of the queried point target by inclusion of observations from all different viewing geometries and robust inversion with 1 norm minimization in a local neighborhood. The method allows for highly detailed and shadow-free 3D deformation monitoring, as has been demonstrated in [54]. An example of seasonal motion decomposition on a small test site in Berlin is demonstrated in Figure 7, where it shows the vertical (up), and horizontal (east-west) linear deformation of a railway bridge.…”

Section: D Motion Decompositionmentioning

confidence: 96%

“…A 3D deformation vector in a geographic coordinate system is highly beneficial to improving the interpretation of the deformation pattern. A 3D motion decomposition algorithm was proposed and validated on four TomoSAR point clouds in [54]. The method relies on either geometrical [39,43] or geodetic fusion [6,55] of multi-aspect TomoSAR point clouds as input.…”

Section: D Motion Decompositionmentioning

confidence: 99%

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A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data

et al. 2018

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Since its launch in 2007, TerraSAR-X has continuously provided spaceborne synthetic aperture radar (SAR) images of our planet with unprecedented spatial resolution, geodetic, and geometric accuracy. This has brought life to the once inscrutable SAR images, which deterred many researchers. Thanks to merits like higher spatial resolution and more precise orbit control, we are now able to indicate individual buildings, even individual floors, to pinpoint targets within centimeter accuracy. As a result, multi-baseline SAR interferometric (InSAR) techniques are flourishing, from point target-based algorithms, to coherent stacking techniques, to absolute positioning of the former techniques. This article reviews the recent advances of multi-baseline InSAR techniques using TerraSAR-X images. Particular focus was put on our own development of persistent scatterer interferometry, SAR tomography, robust estimation in distributed scatterer interferometry and absolute positioning using geodetic InSAR. Furthermore, by introducing the applications associated with these techniques, such as 3D reconstruction and deformation monitoring, this article is also intended to give guidance to wider audiences who would like to resort to SAR data and related techniques for their applications.

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Section: D Motion Decompositionmentioning

confidence: 96%

Section: D Motion Decompositionmentioning

confidence: 99%

A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data

et al. 2018

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“…The white parts with no detected PS include mostly the vegetated areas such as the well known Tierpark situated at the lower left of the center of the scene. Deformation maps of Berlin obtained from TerraSAR-X PSI and SAR tomography have been previously analyzed [4,14,43,44] and are not further discussed here.…”

Section: Berlin Insar and Psi Processingmentioning

confidence: 99%

Geocoding Error Correction for InSAR Point Clouds

2018

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Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.

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“…In [18], clutter statistical models for stacks of very-high-frequency (VHF) wavelength-resolution SAR images are discussed. The SAR image stacks are a frequent topic of study for SAR systems with high resolution [19][20][21]. However, the literature lacks the use of large image stacks for wavelength-resolution SAR for change detection applications.…”

Section: Introductionmentioning

confidence: 99%

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

Palm

Alves²,

Pettersson³

et al. 2020

Sensors

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This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0.11 / km 2 , when considering military vehicles concealed in a forest.

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Three-Dimensional Deformation Monitoring of Urban Infrastructure by Tomographic SAR Using Multitrack TerraSAR-X Data Stacks

Cited by 36 publications

References 31 publications

A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data

A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data

Geocoding Error Correction for InSAR Point Clouds

Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack

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