A Bayesian Deep Learning approach for the estimation of Forest Parameters from Interferometric SAR images

Carcereri, Daniel and Ghio, Federico and Rizzoli, Paola and Tebaldini, Stefano (2025) A Bayesian Deep Learning approach for the estimation of Forest Parameters from Interferometric SAR images. Living Planet Symposium, 2025-06-23 - 2025-06-27, Vienna, Austria.

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Item URL in elib:

https://elib.dlr.de/213931/

Document Type:

Conference or Workshop Item (Poster)

Title:

A Bayesian Deep Learning approach for the estimation of Forest Parameters from Interferometric SAR images

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
Ghio, Federico	UNSPECIFIED	https://orcid.org/0009-0009-0442-0305	UNSPECIFIED
Rizzoli, Paola	UNSPECIFIED	https://orcid.org/0000-0001-9118-2732	UNSPECIFIED
Tebaldini, Stefano	Politecnico di Milano	UNSPECIFIED	UNSPECIFIED

Date:

2025

Refereed publication:

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

deep learning, bayes, forest parameters, TanDEM-X, Gabon

Event Title:

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 17:52

Last Modified:

01 Dec 2025 16:50

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