A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

Wang, Chisheng; Wang, Xiangsheng; Xu, Yaping; Zhang, Bochen; Jiang, Mi; Xiong, Siting; Zhang, Qin; Li, Weidong; Li, Qingquan

doi:10.1109/tgrs.2022.3170567

Cited by 24 publications

(7 citation statements)

References 34 publications

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“…They are added to the representative pixel set, excluding those in the layover, shadow and water body areas, as well as those whose number of SHPs is larger than or equal to 8. The KS test [8] is used for SHP identification, and the coherence matrix of each representative pixel is estimated based on SHPs, as expressed in (5). By averaging the magnitudes of the coherence matrices of all representative pixels, the estimated coherence matrix of the 33 SAR SLC images can be obtained and employed as the adjacency matrix, as shown in Fig.…”

Section: A Terrasar-x Datamentioning

confidence: 99%

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A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

Wang,

Xu,

Zeng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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To achieve accurate interferometric synthetic aperture radar (SAR) phase estimation, it is essential to select appropriate high-coherence interferometric pairs from massive SAR single-look complex (SLC) image data. The selection should include as many high-coherence interferometric pairs as possible while avoiding low-coherence pairs. By combining coherence and spectral clustering, a novel selection method for SAR interferometric pairs is proposed in this paper. The proposed method can be adopted to classify SAR SLC images into different clusters, where the total coherence of interferometric pairs in the same cluster is maximized while that among the different clusters is minimized. This is implemented by averaging the coherence matrices of representative pixels to construct an adjacency matrix and performing eigenvalue decomposition for estimating the number of clusters. The effectiveness of the proposed method is demonstrated using 33 TerraSAR-X and 38 dual-polarization Sentinel-1A data samples, yielding improved topography and deformation monitoring results.

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Section: A Terrasar-x Datamentioning

confidence: 99%

“…They provide more samples for interferometric phase estimation, thus facilitating estimation accuracy enhancement. Therefore, the Multi-Channel InSAR (MCInSAR) [3], [4] and Multi-Temporal InSAR (MTInSAR) [5], [6] techniques have recently received widespread attention.…”

Section: Introductionmentioning

confidence: 99%

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

Wang,

Xu,

Zeng

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…Due to the harsh environment and inconvenient transportation in most areas along with the ATF, conventional geodetic measurements such as Global Positioning System (GPS) and leveling are limited. Interferometric Synthetic Aperture Radar (InSAR) substantially improves the temporal and spatial resolution of surface deformation via stacking individual interferograms, thus providing crustal deformation maps at a decadal scale [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Interseismic Strain across the Western Altyn Tagh Fault from InSAR

2022

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As the northern boundary of the Tibetan Plateau, the long Altyn Tagh fault (ATF) controls the regional tectonic environment, and the study of its long-term fault slip rate is key to understanding the tectonic evolution and deformation of the northern Tibetan Plateau. In this paper, we measure the fault slip rate of the western segment of the ATF using InSAR observations between 2015 to 2020. The Multi-Temporal Interferometric InSAR analysis is applied to obtain the two-dimensional fault-parallel and vertical displacement fields. The spatially dense InSAR observations clearly illustrate the asymmetrical pattern of displacement fields across the fault. Constrained by our InSAR observations, the fault slip rate and locking depth of the western segment of the ATF are inverted using four different models in a Bayesian framework. The two-layer viscoelastic model incorporating lateral heterogeneity of rheology in the lower crust indicates that the fault slip rate of the western ATF is estimated to be 9.8 ± 1.1 mm/yr (at 83.8°E across the ATF) and 8.6 ± 1.1 mm/yr (at 85.1°E), respectively, and the locking depth is 15.8 ± 4.3 km and 14.8 ± 4.9 km. Our new estimates generally agree with the previous estimates of fault slip rate constrained by GPS observations. We conclude that the contrast between the thickness of the elastic layer and the shear modulus of the Tibetan plateau and the Tarim basin jointly contribute to the asymmetric interseismic strain accumulation on the ATF.

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“…The optimum phase of DSs is estimated from the coherence matrix [23]. Recently, a number of works have recognized that coherence errors due to limited ensembles in data statistics affect the accuracy of InSAR measurements for DSs, especially over low coherence scenarios [24][25][26][27][28]. Moreover, although preprocessing of DSs is often simplistically depicted as transforming DSs into PSs, preprocessed DSs are statistically not equivalent to PSs [29].…”

Section: Introductionmentioning

confidence: 99%

Bayesian Estimation of Land Deformation Combining Persistent and Distributed Scatterers

Ding

et al. 2022

Remote Sensing

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Persistent Scatterer Interferometry (PSI) has been widely used for monitoring land deformation in urban areas with millimeter accuracy. In natural terrain, combining persistent scatterers (PSs) and distributed scatterers (DSs) to jointly estimate deformation, such as SqueeSAR, can enhance PSI results for denser and better coverage. However, the phase quality of a large number of DSs is far inferior to that of PSs, which deteriorates the deformation measurement accuracy. To solve the contradiction between measurement accuracy and coverage, a Bayesian estimation method of land deformation combining PSs and DSs is proposed in this paper. First, a two-level network is introduced into the traditional PSI to deal with PSs and DSs. In the first-level network, the Maximum Likelihood Estimation (MLE) of deformation parameters at PSs and high-quality DSs is obtained accurately. In the secondary-level network, the remaining DSs are connected to the nearest PSs or high-quality DSs, and the deformation parameters are estimated by Maximum A Posteriori (MAP) based on Bayesian theory. Due to the poor phase quality of the remaining DSs, MAP can achieve better estimation results than the MLE based on the spatial correlation of the deformation field. Simulation and Sentinel-1A satellite data results verified the feasibility and reliability of the proposed method. Regularized by the spatial deformation field derived from the high-quality PSs and DSs, the proposed method is expected to achieve robust results even in low-coherence areas, such as rural areas, vegetation coverage areas, or deserts.

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A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

Cited by 24 publications

References 34 publications

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

A Method for Selecting SAR Interferometric Pairs Based on Coherence Spectral Clustering

Asymmetric Interseismic Strain across the Western Altyn Tagh Fault from InSAR

Bayesian Estimation of Land Deformation Combining Persistent and Distributed Scatterers

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