Potential of Deep Learning for Forest Height Estimation from TanDEM-X Bistatic InSAR Data

Carcereri, Daniel and Rizzoli, Paola and Ienco, Dino and Bruzzone, Lorenzo (2023) Potential of Deep Learning for Forest Height Estimation from TanDEM-X Bistatic InSAR Data. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2023-07-16 - 2023-07-21, Pasadena, USA.

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Item URL in elib:

https://elib.dlr.de/194610/

Document Type:

Conference or Workshop Item (Speech)

Title:

Potential of Deep Learning for Forest Height Estimation from TanDEM-X Bistatic InSAR 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:

deep learning, forest parameter regression, InSAR

Event Title:

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Event Location:

Pasadena, USA

Event Type:

international Conference

Event Dates:

2023-07-16 - 2023-07-21

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 - Support TerraSAR-X/TanDEM-X operations

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute > Spaceborne SAR Systems
Microwaves and Radar Institute

Deposited By:

Carcereri, Daniel

Deposited On:

05 Apr 2023 09:51

Last Modified:

06 Dec 2023 19:12

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