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Application of LSTM network to predict InSAR-derived time-s..:
, In:
2023 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)
,
Jeon, Woohyun
;
Yi, Jonghyuk
- p. 1-5 , 2023
Link:
https://doi.org/10.1109/CoNTESA61248.2023.10384859
RT T1 <cTitle>2023 International Conference on Computing, Networking, Telecommunications & Engineering Sciences Applications (CoNTESA)</cTitle>: T1 <Title>Application of LSTM network to predict InSAR-derived time-series deformation in the reclamation area of Busan, South Korea</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10384859&Exemplar=1&LAN=DE A1 Jeon, Woohyun A1 Yi, Jonghyuk YR 2023 K1 Deformable models K1 Deep learning K1 Satellite constellations K1 Deformation K1 European Space Agency K1 Predictive models K1 Logic gates K1 PS-InSAR K1 LSTM K1 Deep Learning K1 Sentinel-1 SP 1 OP 5 LK http://dx.doi.org/https://doi.org/10.1109/CoNTESA61248.2023.10384859 DO https://doi.org/10.1109/CoNTESA61248.2023.10384859 SF ELIB - SuUB Bremen
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