Sequential Estimator: Toward Efficient InSAR Time Series Analysis

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

doi:10.1109/tgrs.2017.2711037

Cited by 99 publications

(81 citation statements)

References 27 publications

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“…Finally, the current Earth Observation (EO) scenario is characterized by the availability of large amounts of SAR data over the course of the last 20 years [29,44]. In particular, the new generation SAR missions, such as the Sentinel-1 constellation of the Copernicus SAR satellites and RADARSAT Constellation missions (RCM), are designed with higher spatial resolutions, more systematic wide-area coverage and shorter revisit cycles, which enables global-scale monitoring of the Earth at high temporal and spatial resolutions [29]. The large volume of SAR data will greatly promote the development and applications of InSAR in monitoring ground deformation [29,45].…”

Section: Discussionmentioning

confidence: 99%

“…In this approach, an amplitude-based statistical test (Kolmogorov-Smirnov test) is exploited to adaptively select homogeneous pixels and accurately estimate the covariance matrix; a phase triangulation algorithm, which is based on a maximum likelihood (ML) estimator, is applied to each DS to retrieve the optimized phase estimates of the N−1 phase based on N(N−1)/2 interferograms generated from N SAR images. As demonstrated in [28][29][30][31][32], the SqueeSAR™ approach and its variants can significantly improve the density and quality of InSAR MPs over non-urban areas. However, this DSI techniques also have their drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry

Sun

Jiang

et al. 2018

Remote Sensing

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Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed scatterers (DS), which can identify sufficient measurement points (MPs) when point-wise radar targets are lacking, have great potential to measure such differential reclamation settlement. However, the computational time cost has been the main drawback of current distributed scatterer interferometry(DSI) for its applications compared to the standard PSI analysis. In this paper, we adopted an improved DSI processing strategy for a fast and robust analysis of land subsidence in reclaimed regions, which is characterized by an integration of fast statistically homogeneous pixel selection based (FaSHPS-based) DS detection and eigendecomposition phase optimization. We demonstrate the advantages of the proposed DSI strategy in computational efficiency and deformation estimation reliability by applying it to two TerraSAR-X image data stacks from 2008 to 2009 to retrieve land subsidence over two typical reclaimed regions of Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP). Compared with the state-of-the-art DSI methods, the proposed strategy significantly improves the computational efficiency, which is enhanced approximately 30 times in DS identification and 20 times in phase optimization. On average, the DSI strategy results in 7.8 and 3.7 times the detected number of MPs for HKIA and HKSP with respect to persistent scatter interferometry (PSI), which enables a very detailed characterization of locally differential settlement patterns. Moreover, the DSI-derived results agree well with the levelling survey measurements at HKIA, with a mean difference of 1.87 mm/yr and a standard deviation of 2.08 mm/yr. The results demonstrate that the proposed DSI strategy is effective at improving target density, accuracy and efficiency in monitoring ground deformation, particularly over reclaimed coastal areas.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry

Sun

Jiang

et al. 2018

Remote Sensing

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“…Being MLE, PTA is asymptotically an optimum estimator; it is unbiased and its variance attains the Cramer-Rao Lower Bound (CRLB) for temporal phase estimation [6]. The PTA is however sensitive to the estimation performance of the coherence matrix Γ [7]. The latter is notoriously known to be sub-optimum for low coherence level and small l. PTA estimates the phases through a non-linear optimization scheme with subjective choice of initialization [2].…”

Section: Phase Triangulation Algorithm (Pta)mentioning

confidence: 99%

“…The latter is notoriously known to be sub-optimum for low coherence level and small l. PTA estimates the phases through a non-linear optimization scheme with subjective choice of initialization [2]. Therefore its computational expense poses a challenge to Big Data processing [7].…”

Section: Phase Triangulation Algorithm (Pta)mentioning

confidence: 99%

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

Ansari

Zan

Gomba

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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Multitemporal phase estimation aims at the exploitation temporal data redundancy within the SAR time-series to reduce the impact of inherent stochastic and systematic interferometric phase inconsistencies [1] for distributed scatterers (DS). The consistent phase-series estimated as such is further utilized to retrieve the underlying geophysical and atmospheric signals. Therefore, the precision and interpretability of the retrieved physical signals from the DS is governed by the performance of the phase estimators. Different approaches to phase estimation calls for the investigation of their performance. Here we explain the discrepancy among the different approaches in terms of their underlying covariance model and introduce our recently proposed estimator named EMI [2]. Bridging between different approaches via revised mathematical formulation of phase estimation, EMI enhances the estimation precision and computational efficiency of the temporal phase estimation. The performance of different phase estimators is brought into attention via simulation analysis. Using Sentinel-1 time series over the North and East Anatolian Faults, wide area performance analysis is further carried out and will be presented.

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“…The integration error can easily reach several cm/year in the estimated rates. Careful interferometric processing seems to partially protect from this kind of errors and keep the budget [10]. A safe alternative could be using only permanent/persistent scatterers, avoiding any spatial averaging.…”

Section: Processingmentioning

confidence: 99%

Insar Error Budget for Large Scale Deformation

Zan

Parizzi

González

et al. 2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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The capacity of SAR interferometry to measure surface deformation with accuracy of 1 mm/year or better are well known. However this is typically limited to relative motion at short distance. Thanks to several advances in SAR sensor quality, data availability, orbit determination, processing, and calibration of atmospheric delays it is now possible to achieve that accuracy even across large distances of hundreds of kilometers. In this paper we revise the main contributions to the large scale error, considering available mitigation techniques. We provide a first validation for a processing based on Sentinel-1 data, by comparing our results with GNSS stations. For future SAR's operating at lower frequencies, it is vital to consider ionospheric corrections and likely also the influence of moisture variations in natural scatterers. The choice of processing algorithms, though typically not discussed, can also have a significant effect on the quality of the result.

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Sequential Estimator: Toward Efficient InSAR Time Series Analysis

Cited by 99 publications

References 27 publications

Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry

Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry

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

Insar Error Budget for Large Scale Deformation

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