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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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