Geocoding Error Correction for InSAR Point Clouds

Montazeri, Sina; González, Fernando Rodríguez; Zhu, Xiao Xiang

doi:10.3390/rs10101523

Cited by 19 publications

(17 citation statements)

References 28 publications

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“…For a scatterer located at P(x p , y p , z p ) in terrestrial coordinates with a corresponding zero Doppler coordinate (r, t), we obtain the position state vector S(t) and velocity vector V(t) of the satellite using azimuth time t. The range time is indicated by r. The position P is now determined by solving three equations, called Doppler-Range-Ellipsoid equations, which are defined as [1,6,37,38]…”

Section: Absolute Height Correctionmentioning

confidence: 99%

Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

Leijen

Chang

et al. 2019

Remote Sensing

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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.

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Section: Absolute Height Correctionmentioning

confidence: 99%

Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

Leijen

Chang

et al. 2019

Remote Sensing

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“…The quality check approach proposed in this study for a non-CR PS is based on the extraction of the DSM LiDAR value at the PS position and the comparison of this value with the one obtained by PS processing chain. However, it is worth noting that height errors induce errors in solving of the Doppler-Range-Ellipsoid equations to retrieve the 3D Cartesian coordinates of the target [57]. An error in height estimate results in an error in estimating the absolute location, which in turn will condition the extracted value of the DSM LiDAR.…”

Section: Absolute Location Errormentioning

confidence: 99%

“…Since the sub-pixel precision partly depends on the SCR value [58], the SCR was estimated on each PS with the same method presented in Section 3.1.2 and the results are discussed in Section 4.2.5. Apart from atmospheric path delay, the other sources of errors in absolute positioning, such as the azimuth shift, solid earth tides or tectonics [57,[59][60][61] are not discussed in this study. Thus, a difference between PS height and LiDAR height may be due to an error in height estimation and/or due to an error in the absolute positioning.…”

Section: Absolute Location Errormentioning

confidence: 99%

Integration of Corner Reflectors for the Monitoring of Mountain Glacier Areas with Sentinel-1 Time Series

et al. 2019

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Glacier flow and slope instabilities in Alpine mountain areas represent a hazard issue. Sentinel-1 satellites provide regular Synthetic Aperture Radar (SAR) acquisitions that are potentially useful to monitor these areas, but they can be affected by temporal decorrelation due to rapid changes in the surface. The application of interferometric synthetic aperture radar (InSAR) therefore seems difficult due to loss of coherence. On the other hand, Corner Reflectors (CR) can be used as coherent targets in SAR images for accurate displacement measurement thanks to their strong backscattering property and temporal stability. The use of CRs in multi-temporal InSAR analysis in Alpine mountain areas can thus be beneficial. In this study, we present a comparison between triangular and rectangular CRs, based on Radar Cross Section (RCS) measurements in an anechoic chamber and on long-term experiments over the Argentière glacier and the surrounding slopes and moraine. The visibility in both summer and winter of 10 CRs installed on the test site was investigated. As this area is exposed to heavy precipitation including snow falls, two perforated CRs were tested. The amplitude stability and the phase error of each CR were estimated. A precise tracking of two CRs installed at the glacier surface was also able to measure the displacement of the Argentière glacier, giving results close to previous GPS measurements. Furthermore, a Persistent Scatterer Interferometry (PSI) study was conducted, using the most stable CR as reference point to estimate slope instabilities, which led to the identification of an area corresponding to a tectonic fault called “Faille de l’angle”. The precise absolute locations of the CRs were successfully estimated and PS heights were compared with a LiDAR-based (Light Detection And Ranging) digital elevation model (DEM) and GPS measurements.

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“…For more information on the magnitude of these errors and their effect on each coordinate component of PS, the reader is referred to [3].…”

Section: Introductionmentioning

confidence: 99%

“…The result includes the new corrected PSI point clouds with improved localization accuracy. For more details on each step of the geocoding correction framework, the reader is referred to[3].…”

mentioning

confidence: 99%

Mitigation of Positioning Bias in PSI Point Clouds

Montazeri

González

Zhu

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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In this paper, we propose a framework for geocoding error correction of point clouds obtained from Persistent Scatterer Interferometry (PSI) in urban areas. The horizontal positioning bias of Persistent Scatterers (PS) is mitigated by applying geodetic and atmospheric corrections to the PS azimuth and range timings. Furthermore, the vertical positioning bias, caused due to the unknown height of the PSI reference point, is estimated and compensated for by the use of Synthetic Aperture Radar (SAR)-based Ground Control Point (GCP)s. Experimental results based on cross-heading pairs of TerraSAR-X (TS-X) high resolution spotlight images over the city of Berlin, Germany and in Oulu, Finland are presented. For selected test sites, the localization accuracy of the point clouds is analyzed with respect to a reference LiDAR data, which demonstrates the applicability of the proposed correction approach.

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Geocoding Error Correction for InSAR Point Clouds

Cited by 19 publications

References 28 publications

Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

Monitoring Deformation along Railway Systems Combining Multi-Temporal InSAR and LiDAR Data

Integration of Corner Reflectors for the Monitoring of Mountain Glacier Areas with Sentinel-1 Time Series

Mitigation of Positioning Bias in PSI Point Clouds

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