elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Robust estimators for multipass SAR interferometry

Wang, Yuanyuan and Zhu, Xiao Xiang (2016) Robust estimators for multipass SAR interferometry. IEEE Transactions on Geoscience and Remote Sensing, 54 (2), pp. 968-980. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2015.2471303. ISBN 0196-2892. ISSN 0196-2892.

[img] PDF
1kB

Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7265054&tag=1

Abstract

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.

Item URL in elib:https://elib.dlr.de/103772/
Document Type:Article
Title:Robust estimators for multipass SAR interferometry
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wang, YuanyuanTUMUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Date:2016
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:54
DOI :10.1109/TGRS.2015.2471303
Page Range:pp. 968-980
Editors:
EditorsEmailEditor's ORCID iD
Plaza, Antonio J.aplaza@unex.esUNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
ISBN:0196-2892
Status:Published
Keywords:robust estimation, M-estimator, rank covariance matrix, D-InSAR, InSAR
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Wang, Yuanyuan
Deposited On:08 Apr 2016 15:02
Last Modified:31 Jul 2019 20:01

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.