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On the derivation of forest parameters from Interferometric SAR features using Deep Learning

Carcereri, Daniel and Rizzoli, Paola and Tebaldini, Stefano (2025) On the derivation of forest parameters from Interferometric SAR features using Deep Learning. ESA Living Planet Symposium, 2025-06-23 - 2025-06-27, Vienna, Austria.

Full text not available from this repository.


Item URL in elib:https://elib.dlr.de/213930/
Document Type:Conference or Workshop Item (Speech)
Title:On the derivation of forest parameters from Interferometric SAR features using Deep Learning
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
Tebaldini, StefanoPolitecnico di MilanoUNSPECIFIEDUNSPECIFIED
Date:2025
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:deep learning, forest parameter, TanDEM-X, XAI
Event Title:ESA Living Planet Symposium
Event Location:Vienna, Austria
Event Type:international Conference
Event Start Date:23 June 2025
Event End Date:27 June 2025
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 > Spaceborne SAR Systems
Microwaves and Radar Institute
Deposited By: Carcereri, Daniel
Deposited On:02 Jun 2025 17:57
Last Modified:01 Dec 2025 16:50

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