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Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods

Carcereri, Daniel (2020) Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods. Master's, University of Trento.

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Item URL in elib:https://elib.dlr.de/134289/
Document Type:Thesis (Master's)
Title:Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Carcereri, DanielUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:October 2020
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:74
Status:Published
Keywords:Synthetic Aperture Radar (SAR), Quantization, Data Volume Reduction, Convolutional Neural Network (CNN), Deep Learning
Institution:University of Trento
Department:Information Engineering and Computer Science
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 - Projekt TanDEM-X (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Martone, Michele
Deposited On:02 Mar 2020 11:56
Last Modified:12 Nov 2020 10:41

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