A Deep Learning Approach for Forest Height Estimation using TanDEM-X

Carcereri, Daniel and Rizzoli, Paola and Ienco, Dino and Bruzzone, Lorenzo (2023) A Deep Learning Approach for Forest Height Estimation using TanDEM-X. In: Downloadable conference presentations. TerraSAR-X/TanDEM-X Science Team Meeting, 2023-10-18 - 2023-10-20, Oberpfaffenhofen, Germany.

Full text not available from this repository.

Item URL in elib:

https://elib.dlr.de/200403/

Document Type:

Conference or Workshop Item (Speech)

Title:

A Deep Learning Approach for Forest Height Estimation using TanDEM-X

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

Journal or Publication Title:

Downloadable conference presentations

Refereed publication:

Yes

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

synthetic aperture radar, interferometry, deep learning, forestry

Event Title:

TerraSAR-X/TanDEM-X Science Team Meeting

Event Location:

Oberpfaffenhofen, Germany

Event Type:

international Conference

Event Start Date:

18 October 2023

Event End Date:

20 October 2023

Organizer:

DLR

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:

06 Dec 2023 15:18

Last Modified:

24 Apr 2024 21:00

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