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SURE-Based Regularization Parameter Selection for TomoSAR Imaging via Maximum-Likelihood

Serafín García, Sergio Alejandro and Martin del Campo Becerra, Gustavo Daniel and Ortega Cisneros, Susana and Reigber, Andreas (2020) SURE-Based Regularization Parameter Selection for TomoSAR Imaging via Maximum-Likelihood. In: 21st International Radar Symposium, IRS 2021. International Radar Symposium (IRS), 2020-10-05 - 2020-10-07, Warsaw, Poland. doi: 10.23919/IRS48640.2020.9253844. ISBN 978-394497631-0. ISSN 2155-5753.

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Regularized iterative reconstruction algorithms for Synthetic Aperture Radar (SAR) Tomography (TomoSAR), like the ones based on Maximum Likelihood (ML), offer an accurate estimate of the Power Spectrum Pattern (PSP) displaced along the Perpendicular to the Line-of-Sight (PLOS) direction. The recovered PSP is considered as ‘good-fitted’ or ‘appropriate-fitted’, since the reconstruction fits correctly enough with the position and density of the objectives in the field backscattered towards the sensor. However, the correct functioning of these regularization approaches is constrained to the proper selection of the regularization parameters. Therefore, for such a purpose, this paper suggests using a criterion based on the Stein’s Unbiased Risk Estimate (SURE) strategy. SURE approximates the Mean Square Error (MSE) between the estimated and actual PSP, purely from the measured (observed) data, without the need of any knowledge about the true PSP. Consequently, the proper selection of the regularization parameters corresponds to the minimum SURE value, which guarantees having a ‘good-fitted’ reconstruction. The experiments are performed in simulated data for different representative cases.

Item URL in elib:https://elib.dlr.de/134328/
Document Type:Conference or Workshop Item (Speech)
Title:SURE-Based Regularization Parameter Selection for TomoSAR Imaging via Maximum-Likelihood
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Serafín García, Sergio AlejandroUNSPECIFIEDhttps://orcid.org/0000-0003-2986-3793UNSPECIFIED
Martin del Campo Becerra, Gustavo DanielUNSPECIFIEDhttps://orcid.org/0000-0003-1642-6068UNSPECIFIED
Reigber, AndreasUNSPECIFIEDhttps://orcid.org/0000-0002-2118-5046UNSPECIFIED
Journal or Publication Title:21st International Radar Symposium, IRS 2021
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Keywords:Maximum-Likelihood (ML), Stein’s Unbiased Risk Estimate (SURE), Synthetic Aperture Radar (SAR) Tomography (TomoSAR).
Event Title:International Radar Symposium (IRS)
Event Location:Warsaw, Poland
Event Type:international Conference
Event Dates:2020-10-05 - 2020-10-07
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
Deposited By: Martin del Campo Becerra, Gustavo
Deposited On:04 Mar 2020 11:24
Last Modified:19 Jul 2023 13:14

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