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Robust nonlinear blind SAR tomography in urban areas

Wang, Yuanyuan and Zhu, Xiao Xiang (2018) Robust nonlinear blind SAR tomography in urban areas. In: Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR, pp. 594-599. EUSAR 2018, 04.-07. Juni 2018, Aachen, Germany. ISBN 978-3-8007-4636-1 ISSN 2197-4403

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Synthetic aperture radar has been widely exploited for the reconstruction of 3-D urban models. Resolving the layovered scatterers in SAR images is typically tackled by an explicit inversion of the SAR imaging model, which is otherwise known as SAR tomography (TomoSAR). TomoSAR is essentially a spectral estimation problem. Existing algorithms usually do not have a closed-form solution, rendering them computationally expensive. This paper demonstrates a robust nonlinear blind SAR tomographic method via kernel principle component analysis (KPCA) to unmix the layovered scatterers, avoiding the computationally expensive multidimensional tomographic inversion. We demonstrate that the state-of-the-art linear PCA-based methods are limited by its strict assumption of orthogonal signals, as well as by its assumption of ergodic Gaussian samples that are often violated in urban area. Experiments on real data show that the proposed method outperforms the state-of-the-art by a factor of three in terms of the accuracy of the phase estimates of individual scatterers.

Item URL in elib:https://elib.dlr.de/120439/
Document Type:Conference or Workshop Item (Speech)
Title:Robust nonlinear blind SAR tomography in urban areas
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Date:June 2018
Journal or Publication Title:Proceedings of the European Conference on Synthetic Aperture Radar, EUSAR
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 594-599
Keywords:PCA, kernel PCA, blind source separation, TomoSAR, InSAR, multibaseline, SAR, covariance matrix
Event Title:EUSAR 2018
Event Location:Aachen, Germany
Event Type:international Conference
Event Dates:04.-07. Juni 2018
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 > EO Data Science
Deposited By: Wang, Yuanyuan
Deposited On:20 Jun 2018 13:09
Last Modified:20 Jun 2018 13:09

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