Deep Learning for Forest Canopy Height Estimation from SAR

Mahesh, Ragini Bal and Hänsch, Ronny (2023) Deep Learning for Forest Canopy Height Estimation from SAR. In: 2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2023-07-16 - 2023-07-21, Pasadena, USA. doi: 10.1109/igarss52108.2023.10281899. ISBN 979-835032010-7. ISSN 2153-6996.

Full text not available from this repository.

Item URL in elib:

https://elib.dlr.de/195398/

Document Type:

Conference or Workshop Item (Speech, Poster)

Title:

Deep Learning for Forest Canopy Height Estimation from SAR

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Mahesh, Ragini Bal	UNSPECIFIED	https://orcid.org/0000-0002-2747-9811	189325748
Hänsch, Ronny	UNSPECIFIED	https://orcid.org/0000-0002-2936-6765	UNSPECIFIED

Date:

2023

Journal or Publication Title:

2023 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

Yes

In ISI Web of Science:

DOI:

10.1109/igarss52108.2023.10281899

ISSN:

2153-6996

ISBN:

979-835032010-7

Status:

Published

Keywords:

Deep Learning, Forest Height, InSAR

Event Title:

IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

Event Location:

Pasadena, USA

Event Type:

international Conference

Event Start Date:

16 July 2023

Event End Date:

21 July 2023

Organizer:

IEEE GRSS

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 - Artificial Intelligence

Location:

Oberpfaffenhofen

Institutes and Institutions:

Microwaves and Radar Institute > SAR Technology

Deposited By:

Hänsch, Ronny

Deposited On:

03 Jul 2023 07:29

Last Modified:

06 Aug 2025 15:10

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