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/ | ||||||
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Document Type: | Thesis (Master's) | ||||||
Title: | Estimation of SAR Quantization Error: Potentials of Deep Learning and Classical Methods | ||||||
Authors: |
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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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