Robust Estimators for Multipass SAR Interferometry

This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or w...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 54; H. 2; S. 968 - 980
Hauptverfasser: Wang, Yuanyuan, Zhu, Xiao Xiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.02.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Atmospheric modeling
Covariance matrices
Covariance matrix
Differential interferometric synthetic aperture radar (D-InSAR)
Estimates
Estimators
History
Interferometry
M-estimator
Maximum likelihood estimation
Non-Gaussian
Normal distribution
Optimization
Phase error
rank covariance matrix
robust estimation
robust InSAR optimization (RIO)
Robustness
SAR interferometry (InSAR)
Synthetic aperture radar
SAR interferometry (InSAR)
rank covariance matrix
robust InSAR optimization (RIO)
Differential interferometric synthetic aperture radar (D-InSAR)
robust estimation
M-estimator
ISSN:0196-2892, 1558-0644
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Zusammenfassung:This paper introduces a framework for robust parameter estimation in multipass interferometric synthetic aperture radar (InSAR), such as persistent scatterer interferometry, SAR tomography, small baseline subset, and SqueeSAR. These techniques involve estimation of phase history parameters with or without covariance matrix estimation. Typically, their optimal estimators are derived on the assumption of stationary complex Gaussian-distributed observations. However, their statistical robustness has not been addressed with respect to observations with nonergodic and non-Gaussian multivariate distributions. The proposed robust InSAR optimization (RIO) framework answers two fundamental questions in multipass InSAR: 1) how to optimally treat images with a large phase error, e.g., due to unmolded motion phase, uncompensated atmospheric phase, etc.; and 2) how to estimate the covariance matrix of a non-Gaussian complex InSAR multivariate, particularly those with nonstationary phase signals. For the former question, RIO employs a robust M-estimator to effectively downweight these images; and for the latter, we propose a new method, i.e., the rank M-estimator, which is robust against non-Gaussian distribution. Furthermore, it can work without the assumption of sample stationarity, which is a topic that has not previously been addressed. We demonstrate the advantages of the proposed framework for data with large phase error and heavily tailed distribution, by comparing it with state-of-the-art estimators for persistent and distributed scatterers. Substantial improvement can be achieved in terms of the variance of estimates. The proposed framework can be easily extended to other multipass InSAR techniques, particularly to those where covariance matrix estimation is vital.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2015.2471303