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Parameter Selection Criteria for TomoSAR Focusing

Martin del Campo Becerra, Gustavo Daniel and Serafín García, Sergio Alejandro and Reigber, Andreas and Ortega Cisneros, Susana (2020) Parameter Selection Criteria for TomoSAR Focusing. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.3042661. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/document/9281339

Abstract

The synthetic aperture radar (SAR) tomography (TomoSAR) inverse problem is commonly tackled in the context of the direc-tion-of-arrival estimation theory. The latter allows achieving super-resolution, along with ambiguity levels reduction, thanks to the use of parametric focusing methods, as multiple signal classification (MUSIC), and statistical regularization techniques, like the maximum-likelihood inspired adaptive robust iterative approach (MARIA). Nevertheless, in order to correctly suit the considered signal model, MUSIC and most regularization ap-proaches require an appropriate setting of the involved parame-ters. In both cases, the accuracy of the retrieved solutions de-pends on the right selection of the assigned values. Thus, with the aim of dealing with such an issue, this article addresses sev-eral parameter selection strategies, adapted specifically to the TomoSAR scenario. Parametric techniques as MUSIC solve the TomoSAR problem in a different manner as the regularization methods do, hence, each approach demands different methodol-ogies for the proper estimation of their parameters. Conse-quently, we refer to the Kullback-Leibler information criterion for the model order selection of parametric techniques as MUSIC, whereas we rather explore the Morozov’s discrepancy principle, the L-Curve, the Stein’s unbiased risk estimate and the generalized cross-validation, to choose the regularization pa-rameters. After the incorporation of these criteria to MUSIC and MARIA, respectively, their capabilities are first analyzed through simulations, and later on, utilizing real data acquired from an urban area.

Item URL in elib:https://elib.dlr.de/139067/
Document Type:Article
Title:Parameter Selection Criteria for TomoSAR Focusing
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Martin del Campo Becerra, Gustavo DanielGustavo.MartindelCampoBecerra (at) dlr.dehttps://orcid.org/0000-0003-1642-6068
Serafín García, Sergio Alejandrosergio.serafin (at) cinvestav.mxhttps://orcid.org/0000-0003-2986-3793
Reigber, AndreasAndreas.Reigber (at) dlr.dehttps://orcid.org/0000-0002-2118-5046
Ortega Cisneros, Susanasusana.ortega (at) cinvestav.mxUNSPECIFIED
Date:4 December 2020
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1109/JSTARS.2020.3042661
Editors:
EditorsEmailEditor's ORCID iD
Du, QianDepartment of Electrical and Computer Engineering, Mississippi State University, Mississippi StateUNSPECIFIED
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:Super-resolution of Remotely Sensed Images
ISSN:1939-1404
Status:Published
Keywords:Information criteria, generalized cross-validation, L-Curve, maximum likelihood (ML), model order selection (MOS), syn-thetic aperture radar (SAR) tomography (TomoSAR).
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 - Aircraft SAR
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > SAR Technology
Deposited By: Martin del Campo Becerra, Gustavo
Deposited On:02 Dec 2020 18:56
Last Modified:29 Nov 2021 08:21

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