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Sequential Estimator: Toward Efficient InSAR Time Series Analysis

Ansari, Homa and De Zan, Francesco and Bamler, Richard (2017) Sequential Estimator: Toward Efficient InSAR Time Series Analysis. IEEE Transactions on Geoscience and Remote Sensing, 55 (10), pp. 5637-5652. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/TGRS.2017.2711037. ISSN 0196-2892.

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Official URL: http://ieeexplore.ieee.org/document/8024151/


Wide-swath Synthetic Aperture Radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of Interferometric SAR (InSAR) time series. The processing of the emerging Big-Data is however challenging for the state-of-the-art InSAR analysis techniques. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. Such interferograms are used to link the “older” data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of re-processing the entire data stack at the face of each new acquisition. It is shown that the proposed estimator introduces only negligible degradation compared to the Cramér-Rao-Lower-Bound. The estimator may therefore be adapted for high-precision Near-Real-Time processing of InSAR and accommodate the conversion of InSAR from an off-line to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to the state-of-the-art techniques via simulations and application to Sentinel-1 data.

Item URL in elib:https://elib.dlr.de/109661/
Document Type:Article
Title:Sequential Estimator: Toward Efficient InSAR Time Series Analysis
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Ansari, Homahoma.ansari (at) dlr.deUNSPECIFIED
De Zan, Francescofrancesco.dezan (at) dlr.deUNSPECIFIED
Bamler, Richardrichard.bamler (at) dlr.deUNSPECIFIED
Date:1 September 2017
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:Yes
DOI :10.1109/TGRS.2017.2711037
Page Range:pp. 5637-5652
EditorsEmailEditor's ORCID iD
UNSPECIFIEDIEEE Geoscience and Remote Sensing SocietyUNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Big-Data, Coherence Bias, Data Compression, Distributed Scatterers, Differential Interferometric Synthetic Aperture Radar (DInASR), Low-Rank Approximation, Maximum-Likelihood Estimation, Error Analysis.
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
Remote Sensing Technology Institute > Leitungsbereich MF
Deposited By: Ansari, Homa
Deposited On:16 Dec 2016 15:00
Last Modified:23 Feb 2019 00:21

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