EMI: Efficient Temporal Phase Estimation and its Impact on High-Precision InSAR Time Series Analysis

Ansari, Homa; Zan, Francesco De; Gomba, Giorgio; Bamler, Richard

doi:10.1109/igarss.2019.8900409

Cited by 6 publications

(6 citation statements)

References 10 publications

(18 reference statements)

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“…Various phase-linking approaches are defined based on different modelings of the SCM [15], [16]. In an earlier work, we proposed a computationally efficient approach to phase linking called Eigen-decomposition-based Maximumlikelihood-estimator of Interferometric phase (EMI) [15].…”

Section: Phase-linking Techniquesmentioning

confidence: 99%

Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry

Ansari

Zan

Parizzi

2021

IEEE Trans. Geosci. Remote Sensing

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117

101

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This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms.

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Section: Phase-linking Techniquesmentioning

confidence: 99%

Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry

Ansari

Zan

Parizzi

2021

IEEE Trans. Geosci. Remote Sensing

Self Cite

117

101

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“…We use IERS 2010 convention [58] to correct solid earth tides, ECMWF ERA5 reanalysis data to correct tropospheric delay, and Tec maps CODE data to correct ionospheric delay using SAR Geodesy Processor (SGP) of DLR [59]. Additionally, to overcome the sparsity of identified points for estimation, combined PSI [14]- [16] and DSI [33], [60] are performed to create stacked deformation maps. Processed this way, InSAR deformation maps achieve accuracy in the mm/year-level [34].…”

Section: B Insar Datamentioning

confidence: 99%

Deep Learning for Subtle Volcanic Deformation Detection With InSAR Data in Central Volcanic Zone

Beker,

Ansari,

Montazeri

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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Subtle volcanic deformations point to volcanic activities, and monitoring them helps predict eruptions. Today, it is possible to remotely detect volcanic deformation in mm/year scale thanks to advances in interferometric Synthetic Aperture Radar (InSAR). This paper proposes a framework based on a deep learning model to automatically discriminate subtle volcanic deformations from other deformation types in five-year-long InSAR stacks. Models are trained on a synthetic training set. To better understand and improve the models, explainable AI analyses are performed. In initial models, gradient-weighted Class Activation Mapping (Grad-CAM) linked new-found patterns of slope processes and salt lake deformations to falsepositive detections. The models are then improved by fine-tuning with a hybrid synthetic-real data, and additional performance is extracted by low-pass spatial filtering of the real test set. T-SNE latent feature visualization confirmed the similarity and shortcomings of the fine-tuning set, highlighting the problem of elevation components in residual tropospheric noise. After fine-tuning, all the volcanic deformations are detected, including the smallest one, Lazufre, deforming 5 mm/year. The first time confirmed deformation of Cerro El Condor is observed, deforming 9.9-17.5 mm/year. Finally, sensitivity analysis uncovered the model's minimal detectable deformation of 2 mm/year.

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“…Various phase linking approaches are defined based on different modeling of the SCM [14], [15]. In an earlier work, we proposed a computationally efficient approach to phase linking called Eigen-decomposition-based Maximum-likelihoodestimator of Interferometric phase (EMI) [14].…”

Section: Phase Linking Techniquesmentioning

confidence: 99%

Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

Ansari¹,

Zan²,

Parizzi³

2020

Preprint

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<div>This paper investigates the presence of a new interferometric signal in multilooked Synthetic Aperture Radar (SAR) interferograms which cannot be attributed to atmospheric or earth surface topography changes. The observed signal is short-lived and decays with temporal baseline; however, it is distinct from the stochastic noise usually attributed to temporal decorrelation. The presence of such fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. <br></div><div>The contribution of the mentioned phase component is quantitatively assessed. For short temporal baseline interferograms, we quantify the phase contribution to be in the regime of 5 rad at C-band. The biasing impact on deformation signal retrieval is further evaluated. As an example, exploiting a subset of short temporal baseline interferograms which connects each acquisition with the successive 5 in the time series, a significant bias of -6.5 mm/yr is observed in the estimation of deformation velocity from a four-year Sentinel-1 data stack. A practical solution for mitigation of this physical fading signal is further discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease to -0.24 mm/yr for the Sentinel-1 time series.</div>Based on these analyses, we put forward our recommendations for efficient and accurate deformation signal retrieval from large stacks of multilooked interferograms.

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EMI: Efficient Temporal Phase Estimation and its Impact on High-Precision InSAR Time Series Analysis

Cited by 6 publications

References 10 publications

Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry

Study of Systematic Bias in Measuring Surface Deformation With SAR Interferometry

Deep Learning for Subtle Volcanic Deformation Detection With InSAR Data in Central Volcanic Zone

Study of Systematic Bias in Measuring Surface Deformation with SAR Interferometry

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