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Caushi, Andrea (2026) Deep-learning for soil moisture retrieval from Sentinel-1 InSAR data. Master's, Politecnico di Milano.
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| Item URL in elib: | https://elib.dlr.de/214402/ | ||||||||
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| Document Type: | Thesis (Master's) | ||||||||
| Title: | Deep-learning for soil moisture retrieval from Sentinel-1 InSAR data | ||||||||
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| Date: | March 2026 | ||||||||
| Open Access: | No | ||||||||
| Status: | Unpublished | ||||||||
| Keywords: | Synthetic Aperture Radar, soil moisture, Sentinel-1, deep learning, convolutional neural network, weakly-supervised learning, machine learning | ||||||||
| Institution: | Politecnico di Milano | ||||||||
| Department: | Aerospace Engineering | ||||||||
| 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 - AI4SAR | ||||||||
| Location: | Oberpfaffenhofen | ||||||||
| Institutes and Institutions: | Microwaves and Radar Institute Microwaves and Radar Institute > Spaceborne SAR Systems | ||||||||
| Deposited By: | Bueso Bello, Jose Luis | ||||||||
| Deposited On: | 02 Jun 2025 17:06 | ||||||||
| Last Modified: | 12 Nov 2025 11:21 |
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