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Gobbi, Giorgia (2019) InSAR Parameters Retrieval using Deep Residual Learning. Master's, University of Trento.
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| Item URL in elib: | https://elib.dlr.de/126805/ | ||||||||
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| Document Type: | Thesis (Master's) | ||||||||
| Title: | InSAR Parameters Retrieval using Deep Residual Learning | ||||||||
| Authors: |
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| Date: | October 2019 | ||||||||
| Refereed publication: | Yes | ||||||||
| Open Access: | No | ||||||||
| Status: | Published | ||||||||
| Keywords: | InSAR Phase estimation, Deep Learning, Convolutional Neural Network, Synthetic Aperture Radar | ||||||||
| Institution: | University of Trento | ||||||||
| Department: | Department of 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 - SAR methods | ||||||||
| Location: | Oberpfaffenhofen | ||||||||
| Institutes and Institutions: | Microwaves and Radar Institute > Spaceborne SAR Systems | ||||||||
| Deposited By: | Sica, Dr. Francescopaolo | ||||||||
| Deposited On: | 12 Mar 2019 17:06 | ||||||||
| Last Modified: | 08 Jan 2020 09:56 |
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