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Robust blind scatterer separation in multibaseline InSAR

Wang, Yuanyuan and Zhu, Xiao Xiang (2017) Robust blind scatterer separation in multibaseline InSAR. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 1-4. IEEE Xplore. IGARSS 2017, 23-28 July 2017, Fort Worth, USA.

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The side-looking imaging geometry of synthetic aperture radar (SAR) causes inevitable layover in SAR images. Separating the contributions from different scatterers has been the fundamental for many applications. It is typically solved by explicit inversion of the SAR imaging model to retrieve the scattering profile along the mixed dimension (elevation), which is otherwise known as SAR tomography. This paper proposed a robust blind scatterer separation method to demix the layovered scatterers, avoiding the computationally expensive tomographic inversion. We demonstrate that the state-of-the-art principle component decomposition-based methods are heavily influenced by the nonergodicity of the selected samples, especially in urban area, such as point scatterers appearing often on facades. The proposed method is shown to be more robust than the state-of-the-art. Real data example shows that the proposed method outperforms the state-of-the-art by a factor of three in terms of the accuracy of the retrieved phase.

Item URL in elib:https://elib.dlr.de/112545/
Document Type:Conference or Workshop Item (Speech)
Title:Robust blind scatterer separation in multibaseline InSAR
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wang, YuanyuantumUNSPECIFIED
Zhu, Xiao Xiangdlr-imf/tum-lmfUNSPECIFIED
Journal or Publication Title:2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Page Range:pp. 1-4
Publisher:IEEE Xplore
Keywords:blind source separation, robust, covariance matrix, SAR tomography, multibaseline InSAR, InSAR
Event Title:IGARSS 2017
Event Location:Fort Worth, USA
Event Type:international Conference
Event Dates:23-28 July 2017
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
Deposited By: Wang, Yuanyuan
Deposited On:30 May 2017 16:10
Last Modified:31 Jul 2019 20:09

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