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Seasonal Challenges for Rainforest Mapping with Sentinel-1 Time Series: A Deep Learning Approach

Diniz Dal Molin Junior, Ricardo Simao and Rizzoli, Paola and Thirion-Lefevre, Laetitia and Guinvarc’h, Régis (2024) Seasonal Challenges for Rainforest Mapping with Sentinel-1 Time Series: A Deep Learning Approach. European Conference on Synthetic Aperture Radar (EUSAR), 2024-04-23 - 2024-04-26, Munich, Germany. (Submitted)

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Item URL in elib:https://elib.dlr.de/200110/
Document Type:Conference or Workshop Item (Speech)
Title:Seasonal Challenges for Rainforest Mapping with Sentinel-1 Time Series: A Deep Learning Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Diniz Dal Molin Junior, Ricardo SimaoUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732UNSPECIFIED
Thirion-Lefevre, LaetitiaUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Guinvarc’h, RégisUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:2024
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Submitted
Keywords:interferometric synthetic aperture radar; Sentinel-1 time series; forest mapping; deep learning
Event Title:European Conference on Synthetic Aperture Radar (EUSAR)
Event Location:Munich, Germany
Event Type:international Conference
Event Dates:2024-04-23 - 2024-04-26
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
Deposited By: Diniz Dal Molin Junior, Ricardo Simao
Deposited On:30 Nov 2023 13:19
Last Modified:30 Nov 2023 13:19

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