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Object-based multipass InSAR via robust low-rank tensor decomposition

Kang, Jian and Wang, Yuanyuan and Schmitt, Michael and Zhu, Xiao Xiang (2018) Object-based multipass InSAR via robust low-rank tensor decomposition. IEEE Transactions on Geoscience and Remote Sensing, 56 (6), pp. 3062-3077. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2018.2790480 ISSN 0196-2892

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Official URL: https://ieeexplore.ieee.org/document/8303748/

Abstract

The most unique advantage of multipass synthetic aperture radar interferometry (InSAR) is the retrieval of long-term geophysical parameters, e.g., linear deformation rates, over large areas. Recently, an object-based multipass InSAR framework has been proposed by Kang, as an alternative to the typical single-pixel methods, e.g., persistent scatterer interferometry (PSI), or pixel-cluster-based methods, e.g., SqueeSAR. This enables the exploitation of inherent properties of InSAR phase stacks on an object level. As a follow-on, this paper investigates the inherent low rank property of such phase tensors and proposes a Robust Multipass InSAR technique via Object-based low rank tensor decomposition. We demonstrate that the filtered InSAR phase stacks can improve the accuracy of geophysical parameters estimated via conventional multipass InSAR techniques, e.g., PSI, by a factor of 10-30 in typical settings. The proposed method is particularly effective against outliers, such as pixels with unmodeled phases. These merits, in turn, can effectively reduce the number of images required for a reliable estimation. The promising performance of the proposed method is demonstrated using high-resolution TerraSAR-X image stacks.

Item URL in elib:https://elib.dlr.de/116063/
Document Type:Article
Title:Object-based multipass InSAR via robust low-rank tensor decomposition
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Kang, JianTUMUNSPECIFIED
Wang, Yuanyuantum, Yuanyuan.Wang (at) dlr.dehttps://orcid.org/0000-0002-0586-9413
Schmitt, Michaelm.schmitt (at) tum.deUNSPECIFIED
Zhu, Xiao Xiangxiao.zhu (at) dlr.deUNSPECIFIED
Date:28 February 2018
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:56
DOI :10.1109/TGRS.2018.2790480
Page Range:pp. 3062-3077
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:InSAR, multipass, decomposition
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > SAR Signal Processing
Remote Sensing Technology Institute > EO Data Science
Deposited By: Häberle, Matthias
Deposited On:04 Dec 2017 11:10
Last Modified:25 Jul 2019 12:59

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