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From natural images to spaceborne imagery: an empirical study of self-supervised learning for Earth observation

Wang, Yi and Ait Ali Braham, Nassim and Albrecht, Conrad M and Mou, LiChao and Zhu, Xiao Xiang (2022) From natural images to spaceborne imagery: an empirical study of self-supervised learning for Earth observation. LPS 2022, 23.-27. May 2022, Bonn, Germany.

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Abstract

In this work, we provide an empirical study on the performance of self-supervised learning for spaceborne imagery. Specifically, we conduct extensive experiments on three well-known remote sensing datasets BigEarthNet, SEN12MS and LCZ42 using four representative state-of-the-art SSL algorithms MoCo, SwAV, SimSiam and Barlow Twins. We analyze the performance of SSL algorithms under different data regimes and compare them to vanilla supervised learning. In addition, we explore the impact of data augmentation, which is known to be a key component in the design and tuning of modern SSL methods.

Item URL in elib:https://elib.dlr.de/186655/
Document Type:Conference or Workshop Item (Poster)
Title:From natural images to spaceborne imagery: an empirical study of self-supervised learning for Earth observation
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Wang, YiYi.Wang (at) dlr.deUNSPECIFIED
Ait Ali Braham, NassimNassim.AitAliBraham (at) dlr.deUNSPECIFIED
Albrecht, Conrad MConrad.Albrecht (at) dlr.deUNSPECIFIED
Mou, LiChaoLiChao.Mou (at) dlr.deUNSPECIFIED
Zhu, Xiao Xiangxiaoxiang.zhu (at) dlr.deUNSPECIFIED
Date:2022
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:self-supervised learning, Earth observation, remote sensing
Event Title:LPS 2022
Event Location:Bonn, Germany
Event Type:international Conference
Event Dates:23.-27. May 2022
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 - Artificial Intelligence
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Wang, Yi
Deposited On:14 Jun 2022 11:48
Last Modified:20 Jun 2022 14:20

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