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Towards Feature Enhanced SAR Tomography: A Maximum-Likelihood Inspired Approach

Martin del Campo Becerra, Gustavo Daniel and Nannini, Matteo and Reigber, Andreas (2018) Towards Feature Enhanced SAR Tomography: A Maximum-Likelihood Inspired Approach. IEEE Geoscience and Remote Sensing Letters, pp. 1730-1734. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/LGRS.2018.2858571 ISSN 1545-598X

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Abstract

One of the main objectives of the upcoming space missions, such as Tandem-L and BIOMASS, is to map, on a global scale, the forest structure by means of synthetic aperture radar (SAR) tomography (TomoSAR). On one hand, the number of baselines is constrained to the revisit time that avoids temporal decorrelation issues. On the other hand, enhanced resolution is desired, since the forest structure is characterized from the vegetation layers that compose it, reflected in the tomographic profiles as local maxima. The TomoSAR nonlinear ill-conditioned inverse problem is conventionally tackled within the direction-of-arrival (DOA) estimation framework. The DOA-inspired nonparametric techniques are well suited to cope with distributed targets; nonetheless, the achievable resolution highly depends on the span of the tomographic aperture. Alternatively, superresolved parametric approaches have the main drawback related to the white noise model assumption that guaranties the separation of the signal and noise subspaces. Overcoming the disadvantages of the aforementioned techniques, in this letter, we address a novel maximum-likelihood (ML) inspired adaptive robust iterative approach (MARIA) for feature-enhanced TomoSAR reconstruction. MARIA performs resolution enhancement, with suppression of artifacts and ambiguity levels reduction, to an initial estimate of the continuous power spectrum pattern. After convergence, an accurate location of the closely spaced phase centers is achieved, easing the characterization of the forest structure. The feature-enhancing capabilities of the proposed approach are corroborated using airborne F-SAR data of the German Aerospace Center (DLR).

Item URL in elib:https://elib.dlr.de/122179/
Document Type:Article
Title:Towards Feature Enhanced SAR Tomography: A Maximum-Likelihood Inspired Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Martin del Campo Becerra, Gustavo DanielGustavo.MartindelCampoBecerra (at) dlr.dehttps://orcid.org/0000-0003-1642-6068
Nannini, Matteomatteo.nannini (at) dlr.deUNSPECIFIED
Reigber, AndreasAndreas.Reigber (at) dlr.dehttps://orcid.org/0000-0002-2118-5046
Date:1 November 2018
Journal or Publication Title:IEEE Geoscience and Remote Sensing Letters
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI :10.1109/LGRS.2018.2858571
Page Range:pp. 1730-1734
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1545-598X
Status:Published
Keywords:Maximum-likelihood (ML), spectral analysis (SA), synthetic 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 - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Flugzeug-SAR
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > SAR Technology
Deposited By: Martin del Campo Becerra, Gustavo
Deposited On:12 Oct 2018 10:34
Last Modified:05 Nov 2018 15:11

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