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Statistical Regularization for Enhanced TomoSAR Imaging

Martin del Campo Becerra, Gustavo and Nannini, Matteo and Reigber, Andreas (2020) Statistical Regularization for Enhanced TomoSAR Imaging. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2020.2970595. ISSN 1939-1404.

[img] PDF - Postprint version (accepted manuscript)

Official URL: http://www.grss-ieee.org/publication-category/jstars/


One of the main topics in synthetic aperture radar (SAR) tomography (TomoSAR) is the estimation of the vertical structures’ location, which scatter the field back towards the sensor, constrained to a reduced number of passes. Moreover, the introduction of artifacts and the increase of the ambiguity levels due to irregular sampling, consequence of non-uniform acquisition constellations, complicate the accurate estimation of the source parameters. Pursuing the alleviation of such drawbacks, the use of statistical regularization approaches, based on the maximum-likelihood (ML) estimation theory, has been successfully demonstrated in the previous related studies. However, these techniques are constrained to the assumption that the probability density function (pdf) of the observed data is Gaussian. In this paper, in order to solve the ill-posed non-linear TomoSAR inverse problem, we relax this assumption and apply the weighted covariance fitting (WCF) criterion instead. The latter alleviates the previously mentioned drawbacks, and retrieves a power spectrum pattern (PSP) with an outline more similar to the expected one, i.e., recovered using matched spatial filtering (MSF) with a higher number of tracks. First, we present the mathematical background of the related regularization methods, adapted to solve the TomoSAR inverse problem, from which we derive our novel technique, named WISE (WCF-based Iterative Spectral Estimator). Then, the differences and similarities between the addressed regularization approaches are discussed, besides their main advantages and disadvantages. Finally, the implementation details of WISE are treated, along with simulated examples and experimental results gotten from a forested test site.

Item URL in elib:https://elib.dlr.de/133950/
Document Type:Article
Title:Statistical Regularization for Enhanced TomoSAR Imaging
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Martin del Campo Becerra, GustavoUNSPECIFIEDhttps://orcid.org/0000-0003-1642-6068UNSPECIFIED
Nannini, MatteoUNSPECIFIEDhttps://orcid.org/0000-0003-3523-9639UNSPECIFIED
Reigber, AndreasUNSPECIFIEDhttps://orcid.org/0000-0002-2118-5046UNSPECIFIED
Date:25 February 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 ISI Web of Science:Yes
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Keywords:Maximum-likelihood (ML), power spectrum pattern (PSP), spectral analysis (SA), synthetic aperture radar (SAR) tomography (TomoSAR), weighted covariance fitting (WCF).
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:03 Feb 2020 07:38
Last Modified:28 Mar 2023 23:55

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