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Optimization of Deep Residual Learning Architectures for Interferometric Synthetic Aperture Radar (InSAR) Parameters Estimation

Soledade Matos Amorim, Vinícius (2024) Optimization of Deep Residual Learning Architectures for Interferometric Synthetic Aperture Radar (InSAR) Parameters Estimation. Bachelor's, Instituto Tecnológico de Aeronáutica (ITA), Brazil.

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Item URL in elib:https://elib.dlr.de/194672/
Document Type:Thesis (Bachelor's)
Title:Optimization of Deep Residual Learning Architectures for Interferometric Synthetic Aperture Radar (InSAR) Parameters Estimation
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Soledade Matos Amorim, ViníciusITAUNSPECIFIEDUNSPECIFIED
Date:2024
Journal or Publication Title:Optimization of Deep Residual Learning Architectures for Interferometric Synthetic Aperture Radar (InSAR) Parameters Estimation
Refereed publication:Yes
Open Access:No
Status:Unpublished
Keywords:Synthetic Aperture Radar (SAR), SAR Interferometry (InSAR), TanDEM-X, artificial intelligence, deep residual learning
Institution:Instituto Tecnológico de Aeronáutica (ITA), Brazil
Department:Electronic 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 - Support TerraSAR-X/TanDEM-X operations
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > Spaceborne SAR Systems
Deposited By: Dell Amore, Luca
Deposited On:17 Apr 2023 06:27
Last Modified:07 Dec 2023 18:36

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