On the influence of sub-pixel position correction for PS localization accuracy and time series quality

Yang, Mengshi; Dheenathayalan, Prabu; López‐Dekker, Paco; Leijen, Freek van; Liao, Mingsheng; Hanssen, Ramon F.

doi:10.1016/j.isprsjprs.2020.04.023

Cited by 8 publications

(7 citation statements)

References 14 publications

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“…Between the adjacent images, most of the moving targets move in both the azimuth and range directions within a resolution cell, which results in an additional phase shift. This mechanism is similar to that of persistent scatter interferometry [27].…”

Section: Interferometric Phase Statisticssupporting

confidence: 64%

Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

Peng

et al. 2021

Remote Sensing

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Spaceborne synthetic aperture radar (SAR) can provide ground area monitoring with large coverage. However, achieving a wide observation scope comes at the cost of resolution reduction owing to the trade-off between these parameters in conventional SAR. In low-resolution imaging, the moving target appears unresolved, weakly scattered, and slow moving in the image sequence, which can be generated by the subaperture technique. This article proposes a novel moving target detection method. First, interferometric phase statistics are combined with the generalized likelihood ratio test detector. A pixel tracking strategy is further exploited to determine whether a motion signal is present. These methods rely on the approximation of both clutter and noise statistics using Gaussian distributions in a low-resolution scenario. In addition, the motion signals are imaged with a subpixel offset. The proposed method is primarily validated using four real image sequences from TerraSAR-X data, which represent two types of homogeneous areas. The results reveal that moving targets can be detected in nearby areas using this strategy. The method is compared with the stack averaged coherence change detection and particle-filter-based tracking strategies.

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Section: Interferometric Phase Statisticssupporting

confidence: 64%

Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

Peng

et al. 2021

Remote Sensing

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“…In addition to addressing these challenges, many new MT-InSAR methods or approaches also focus on precise parameter estimation or postprocessing [59,60]. For example, to enhance the quality of MT-InSAR results, much effort have been invested into modelling and mitigating, e.g., satellite orbital errors [61][62][63][64], atmospheric delays [65][66][67][68][69], ionospheric effects [70][71][72][73], DEM errors [59,74,75], co-registration errors [76][77][78][79], unwrapping errors [80][81][82], and errors in geocoding the measurements [83,84]. Several variations of MT-InSAR have been developed and implemented in software packages to address these problems and issues, including IPTA [49], STUN-PS [54], StaMPS [85], EMCF-SBAS [55], SqueeSAR [48], CPT [86], PSP [87], TCP-InSAR [56,88], CAESAR [89], CSI [90], LiCSAR [91], and D-TomoSAR [92].…”

Section: Spaceborne Insarmentioning

confidence: 99%

Radar Interferometry for Urban Infrastructure Stability Monitoring: From Techniques to Applications

Wu,

Zhang,

Ding

et al. 2023

Sustainability

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Urban infrastructure is an important part of supporting the daily operation of a city. The stability of infrastructure is subject to various deformations related to disasters, engineering activities, and loadings. Regular monitoring of such deformations is critical to identify potential risks to infrastructure and take timely remedial actions. Among the advanced geodetic technologies available, radar interferometry has been widely used for infrastructure stability monitoring due to its extensive coverage, high spatial resolution, and accurate deformation measurements. Specifically, spaceborne InSAR and ground-based radar interferometry have become increasingly utilized in this field. This paper presents a comprehensive review of both technologies for monitoring urban infrastructures. The review begins by introducing the principles and their technical development. Then, a bibliometric analysis and the major advancements and applications of urban infrastructure monitoring are introduced. Finally, the paper identifies several challenges associated with those two radar interferometry technologies for monitoring urban infrastructure. These challenges include the inconsistent in the distribution of selected measurements from different methods, obstacles arising from rapid urbanization and geometric distortion, specialized monitoring techniques for distinct urban features, long-term deformation monitoring, and accurate interpretation of deformation. It is important to carry out further research to tackle these challenges effectively.

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“…Acquisition metadata (PRF, RSR, τ 0 and t 0 ) and precise satellite orbit state vectors suffice for the closed solution of Equation (6). Comparing the reflector's radar coordinates determined by the GNSS with the coordinates observed in the SAR images, significant differences can occur due to [9,10,33,34]:…”

Section: Sar Positioningmentioning

confidence: 99%

“…where ∆a pq is the integer ambiguity, ∆h pq and d LOS,pq are the residual height difference and the line-of-sight (LOS) displacement between the points, respectively, C pq is the phase constant due to the different atmospheric delay during the master epoch, B ⊥ is the perpendicular baseline, R is the approximate slant range, θ is the local incidence angle, ∆φ subPix is the phase due to the erroneous reference phase removal caused by the sub-pixel position of the points, ∆φ APS is the phase due to the slave Atmospheric Phase Screen (APS) difference, ∆φ clutter is the phase due to the clutter at the points, and ∆φ noise is the phase due to the sensor noise. For arcs connecting the CR, the residual height and the sub-pixel position components can be fully removed [34].…”

Section: Sar Interferometrymentioning

confidence: 99%

GECORIS: An Open-Source Toolbox for Analyzing Time Series of Corner Reflectors in InSAR Geodesy

2021

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Artificial radar reflectors, such as corner reflectors or transponders, are commonly used for radiometric and geometric Synthetic Aperture Radar (SAR) sensor calibration, SAR interferometry (InSAR) applications over areas with few natural coherent scatterers, and InSAR datum connection and geodetic integration. Despite the current abundance of regular SAR time series, no free and open-source software (FOSS) dedicated to analyzing SAR time series of artificial radar reflectors exists. In this paper, we present a FOSS Python toolbox for efficient and automatic estimation of: (i) the clutter level of a particular site before a corner reflector installation, (ii) the Radar Cross Section (RCS) to track a corner reflector’s performance and detect outliers, for example, due to damage or debris accumulation, (iii) the Signal-to-Clutter Ratio (SCR) to predict the positioning precision and the InSAR phase variance, (iv) the InSAR displacement time series of a corner reflector network. We use the toolbox to analyze Sentinel-1 SAR time series of the network of 23 corner reflectors for InSAR monitoring of landslides in Slovakia.

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On the influence of sub-pixel position correction for PS localization accuracy and time series quality

Cited by 8 publications

References 14 publications

Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

Phase-Based GLRT Detection of Moving Targets with Pixel Tracking in Low-Resolution SAR Image Sequences

Radar Interferometry for Urban Infrastructure Stability Monitoring: From Techniques to Applications

GECORIS: An Open-Source Toolbox for Analyzing Time Series of Corner Reflectors in InSAR Geodesy

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