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A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR

Shi, Yilei and Zhu, Xiao Xiang and Yin, Wotao and Bamler, Richard (2018) A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR. IEEE Transactions on Geoscience and Remote Sensing, 56 (10), pp. 6148-6158. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TGRS.2018.2832721 ISSN 0196-2892

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Official URL: https://ieeexplore.ieee.org/document/8412239

Abstract

L1 regularization is used for finding sparse solutions to an underdetermined linear system. As sparse signals are widely expected in remote sensing, this type of regularization scheme and its extensions have been widely employed in many remote sensing problems, such as image fusion, target detection, image super-resolution, and others, and have led to promising results. However, solving such sparse reconstruction problems is computationally expensive and has limitations in its practical use. In this paper, we proposed a novel efficient algorithm for solving the complex-valued L1 regularized least squares problem. Taking the high-dimensional tomographic synthetic aperture radar (TomoSAR) as a practical example, we carried out extensive experiments, both with the simulation data and the real data, to demonstrate that the proposed approach can retain the accuracy of the second-order methods while dramatically speeding up the processing by one or two orders. Although we have chosen TomoSAR as the example, the proposed method can be generally applied to any spectral estimation problems.

Item URL in elib:https://elib.dlr.de/124193/
Document Type:Article
Additional Information:so2sat; relevancy 4;
Title:A fast and accurate basis pursuit denoising algorithm with application to super-resolving tomographic SAR
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Shi, YileiTU-MünchenUNSPECIFIED
Zhu, Xiao XiangDLR-IMF/TUM-LMFUNSPECIFIED
Yin, WotaoUCLAUNSPECIFIED
Bamler, RichardDLR-IMF/TUM-LMFUNSPECIFIED
Date:May 2018
Journal or Publication Title:IEEE Transactions on Geoscience and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:56
DOI :10.1109/TGRS.2018.2832721
Page Range:pp. 6148-6158
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:0196-2892
Status:Published
Keywords:L1 regularization, TomoSAR, basis pursuit denoising (BPDN), second order cone programming (SOCP), proximal gradient (PG)
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 hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Remote Sensing Technology Institute > Leitungsbereich MF
Deposited By: Wang, Yuanyuan
Deposited On:05 Dec 2018 12:42
Last Modified:23 Feb 2019 00:23

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