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Feature Attribution Methods for Multivariate Time-Series Ex..:

, In: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium,

Najjar, Hiba ; Helber, Patrick ; Bischke, Benjamin... - p. 5014-5017 , 2023

Link: https://doi.org/10.1109/IGARSS52108.2023.10282120

RT 
T1 <cTitle>IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium</cTitle>: T1 <Title>Feature Attribution Methods for Multivariate Time-Series Explainability in Remote Sensing</Title>

UL https://suche.suub.uni-bremen.de/peid=ieee-10282120&Exemplar=1&LAN=DE
				
A1 Najjar, Hiba
A1 Helber, Patrick
A1 Bischke, Benjamin
A1 Habelitz, Peter
A1 Sanchez, Cristhian
A1 Mena, Francisco
A1 Miranda, Miro
A1 Pathak, Deepak
A1 Siddamsetty, Jayanth
A1 Arenas, Diego
A1 Vollmer, Michaela
A1 Charfuelan, Marcela
A1 Nuske, Marlon
A1 Dengel, Andreas
YR 2023
SN 2153-7003
K1 Deep learning
K1 Sensitivity
K1 Satellites
K1 Recurrent neural networks
K1 Data models
K1 Sensors
K1 Satellite images
K1 explainable AI
K1 feature attribution
K1 remote sensing
K1 Sentinel-2
K1 crop yield
K1 multivariate time-series
K1 pixel-wise prediction
SP 5014
OP 5017
LK http://dx.doi.org/https://doi.org/10.1109/IGARSS52108.2023.10282120
DO https://doi.org/10.1109/IGARSS52108.2023.10282120
SF ELIB - SuUB Bremen

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