A Novel Deep-Learning-Based Framework for InSAR Parameters Enhancement

Dell Amore, Luca and costa, giovanni and Carcereri, Daniel and Demir, Begüm and Rizzoli, Paola (2025) A Novel Deep-Learning-Based Framework for InSAR Parameters Enhancement. ESA Living Planet Symposium, 2025-06-22, Wien, Österreich.

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Item URL in elib:

https://elib.dlr.de/213925/

Document Type:

Conference or Workshop Item (Poster)

Title:

A Novel Deep-Learning-Based Framework for InSAR Parameters Enhancement

Authors:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Dell Amore, Luca	UNSPECIFIED	https://orcid.org/0000-0002-6731-1300	UNSPECIFIED
costa, giovanni	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Carcereri, Daniel	UNSPECIFIED	https://orcid.org/0000-0002-3956-1409	UNSPECIFIED
Demir, Begüm	Technical University of Berlin	UNSPECIFIED	UNSPECIFIED
Rizzoli, Paola	UNSPECIFIED	https://orcid.org/0000-0001-9118-2732	UNSPECIFIED

Date:

2025

Refereed publication:

Open Access:

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Accepted

Keywords:

InSAR phase, coherence, denoising, deep learning

Event Title:

ESA Living Planet Symposium

Event Location:

Wien, Österreich

Event Type:

international Conference

Event Date:

22 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
Microwaves and Radar Institute > Spaceborne SAR Systems

Deposited By:

Rizzoli, Paola

Deposited On:

02 Jun 2025 17:57

Last Modified:

02 Jun 2025 17:57

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