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Assessing Sentinel-1 InSAR Short-Time-Series for Systematic Rainforest Mapping with Deep Learning

Diniz Dal Molin Junior, Ricardo Simao and Rizzoli, Paola (2022) Assessing Sentinel-1 InSAR Short-Time-Series for Systematic Rainforest Mapping with Deep Learning. European Conference on Synthetic Aperture Radar (EUSAR), 2022-07-25 - 2022-07-27, Leipzig, Germany.

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Item URL in elib:https://elib.dlr.de/186373/
Document Type:Conference or Workshop Item (Speech)
Title:Assessing Sentinel-1 InSAR Short-Time-Series for Systematic Rainforest Mapping with Deep Learning
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Diniz Dal Molin Junior, Ricardo SimaoUNSPECIFIEDUNSPECIFIED
Rizzoli, PaolaUNSPECIFIEDhttps://orcid.org/0000-0001-9118-2732
Date:2022
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Accepted
Keywords:Forest mapping; Deep Learning; Synthetic Aperture Radar (SAR); Sentinel-1
Event Title:European Conference on Synthetic Aperture Radar (EUSAR)
Event Location:Leipzig, Germany
Event Type:international Conference
Event Dates:2022-07-25 - 2022-07-27
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: Diniz Dal Molin Junior, Ricardo Simao
Deposited On:16 May 2022 06:13
Last Modified:16 May 2022 06:13

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