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Deep Learning for forest height estimation from InSAR data: potential and challenges using TanDEM-X and Sentinel-1 data

Carcereri, Daniel and Rizzoli, Paola and Ienco, Dino and Bruzzone, Lorenzo (2023) Deep Learning for forest height estimation from InSAR data: potential and challenges using TanDEM-X and Sentinel-1 data. In: FRINGE Online Abstracts. ESA FRINGE Workshop, 2023-09-11 - 2023-09-15, Leeds, UK.

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Item URL in elib:https://elib.dlr.de/197403/
Document Type:Conference or Workshop Item (Speech, Poster)
Title:Deep Learning for forest height estimation from InSAR data: potential and challenges using TanDEM-X and Sentinel-1 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:2023
Journal or Publication Title:FRINGE Online Abstracts
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:Forest height, deep learning, TanDEM-X, Sentinel-1
Event Title:ESA FRINGE Workshop
Event Location:Leeds, UK
Event Type:international Conference
Event Start Date:11 September 2023
Event End Date:15 September 2023
Organizer:ESA
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: Rizzoli, Paola
Deposited On:22 Sep 2023 11:56
Last Modified:24 Apr 2024 20:57

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