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Applications and challenges of deep learning approaches for forest height estimation from InSAR products

Carcereri, Daniel and Rizzoli, Paola and Ienco, Dino and Bruzzone, Lorenzo (2023) Applications and challenges of deep learning approaches for forest height estimation from InSAR products. In: BioGeoSAR Book of Abstracts. ESA BioGeoSAR Workshop, 2023-11-15 - 2023-11-17, Rome, Italy.

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Item URL in elib:https://elib.dlr.de/197677/
Document Type:Conference or Workshop Item (Speech)
Title:Applications and challenges of deep learning approaches for forest height estimation from InSAR products
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:BioGeoSAR Book of Abstracts
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:bistatic coherence, convolutional neural network, deppe learning, forest height, syntetic aperture radar, synthetic radar interferometry, TanDEM-X
Event Title:ESA BioGeoSAR Workshop
Event Location:Rome, Italy
Event Type:international Conference
Event Dates:2023-11-15 - 2023-11-17
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: Carcereri, Daniel
Deposited On:04 Oct 2023 15:57
Last Modified:19 Jan 2024 14:23

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