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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:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Dell Amore, LucaUNSPECIFIEDhttps://orcid.org/0000-0002-6731-1300UNSPECIFIED
costa, giovanniUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Carcereri, DanielUNSPECIFIEDhttps://orcid.org/0000-0002-3956-1409UNSPECIFIED
Demir, BegümTechnical University of BerlinUNSPECIFIEDUNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Date:2025
Refereed publication:No
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
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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