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Efficient Phase Estimation for Interferogram Stacks

Ansari, Homa and De Zan, Francesco and Bamler, Richard (2018) Efficient Phase Estimation for Interferogram Stacks. IEEE Transactions on Geoscience and Remote Sensing, 56 (7), pp. 4109-4125. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2018.2826045. ISSN 0196-2892.

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Official URL: https://ieeexplore.ieee.org/document/8365087


Signal decorrelation poses a limitation to multipass SAR interferometry. In pursuit of overcoming this limitation to achieve high-precision deformation estimates, different techniques have been developed; with SBAS, SqueeSAR and CAESAR as the overarching schemes. These different analysis approaches raise the question of their efficiency and limitation in phase and consequently deformation estimation. This contribution firstly addresses this question and secondly proposes a new estimator with improved performance. Called Eigendecomposition based Maximum-likelihood-estimator of Interferometric phase (EMI), the proposed estimator combines the advantages of the state-of-the-art techniques. Identical to CAESAR, EMI is solved using Eigendecomposition; it is therefore computationally efficient and straightforward in implementation. Similar to SqueeSAR, EMI is a maximum-likelihood-estimator; hence it retains estimation efficiency. The computational and estimation efficiency of EMI renders it as an optimum choice for phase estimation. A further marriage of EMI with the proposed Sequential Estimator of [1] provides an efficient processing scheme tailored to the analysis of Big InSAR Data. EMI is formulated and verified in relation to the state-of-the-art approaches via mathematical formulation, simulation analysis and experiments with time series of Sentinel-1 data over the volcanic island of Vulcano, Italy.

Item URL in elib:https://elib.dlr.de/116285/
Document Type:Article
Title:Efficient Phase Estimation for Interferogram Stacks
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ansari, HomaHoma.Ansari (at) dlr.dehttps://orcid.org/0000-0002-4549-2497
De Zan, Francescofrancesco.dezan (at) dlr.deUNSPECIFIED
Bamler, Richardrichard.bamler (at) dlr.deUNSPECIFIED
Date:7 July 2018
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2018.2826045
Page Range:pp. 4109-4125
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Big Data, coherence matrix, covariance estimation, differential interferometric synthetic aperture radar (DIn-SAR), distributed scatterers, efficiency, error analysis, maximum-likelihood estimation, near real time processing
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 Tandem-L Vorstudien (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Deposited By: Ansari, Homa
Deposited On:04 Dec 2017 11:32
Last Modified:30 Nov 2018 11:36

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