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. 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 (Poster)

Title:

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

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Carcereri, Daniel	UNSPECIFIED	https://orcid.org/0000-0002-3956-1409	UNSPECIFIED
Rizzoli, Paola	UNSPECIFIED	https://orcid.org/0000-0001-9118-2732	UNSPECIFIED
Ienco, Dino	UNSPECIFIED	https://orcid.org/0000-0002-8736-3132	UNSPECIFIED
Bruzzone, Lorenzo	UNSPECIFIED	https://orcid.org/0000-0002-6036-459X	UNSPECIFIED

Date:

2023

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

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 Dates:

2023-09-11 - 2023-09-15

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:

22 Sep 2023 11:56

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