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A country-level deep-learning approach for canopy height estimation from TanDEM-X InSAR data

Carcereri, Daniel and Rizzoli, Paola and Ienco, Dino and Bruzzone, Lorenzo (2024) A country-level deep-learning approach for canopy height estimation from TanDEM-X InSAR data. European Conference on Synthetic Aperture Radar (EUSAR), 2024-04-23 - 2024-04-26, Munich, Germany. (Submitted)

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Item URL in elib:https://elib.dlr.de/197678/
Document Type:Conference or Workshop Item (Speech)
Title:A country-level deep-learning approach for canopy height estimation from TanDEM-X InSAR data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Carcereri, DanielUNSPECIFIEDhttps://orcid.org/0000-0002-3956-1409UNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Ienco, DinoUNSPECIFIEDhttps://orcid.org/0000-0002-8736-3132UNSPECIFIED
Bruzzone, LorenzoUNSPECIFIEDhttps://orcid.org/0000-0002-6036-459XUNSPECIFIED
Date:2024
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Submitted
Keywords:bistatic coherence, convolutional neural network, deep learning, forest height, synthetic aperture radar, synthetic aperture radar interferometry, TanDEM-X
Event Title:European Conference on Synthetic Aperture Radar (EUSAR)
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:2024-04-23 - 2024-04-26
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: Carcereri, Daniel
Deposited On:09 Oct 2023 08:41
Last Modified:09 Oct 2023 08:46

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