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/ | |||||||||||||||
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Document Type: | Article | |||||||||||||||
Title: | Object-based multipass InSAR via robust low-rank tensor decomposition | |||||||||||||||
Authors: |
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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 - Earth Observation | |||||||||||||||
DLR - Research theme (Project): | R - Vorhaben hochauflösende Fernerkundungsverfahren (old) | |||||||||||||||
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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