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000909743 0247_ $$2doi$$a10.1109/IGARSS46834.2022.9884699
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000909743 020__ $$a978-1-6654-2792-0
000909743 037__ $$aFZJ-2022-03382
000909743 1001_ $$0P:(DE-Juel1)187558$$aSharma, Surbhi$$b0$$ufzj
000909743 1112_ $$aIEEE International Geoscience and Remote Sensing Symposium$$cKuala Lumpur$$d2022-07-17 - 2022-07-22$$gIGARSS 2022$$wMalaysia
000909743 245__ $$aImproving Generalization for Few-Shot Remote Sensing Classification with Meta-Learning
000909743 260__ $$c2022
000909743 300__ $$a5061-5064
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000909743 520__ $$aIn Remote Sensing (RS) classification, generalization ability is one of the measure that characterizes the success of Machine Learning (ML) models, but is often impeded by the scarce availability of annotated training data. Annotated RS samples are expensive to obtain and can present large disparities when produced by different annotators. In this paper, we utilize Few-Shot Learning (FSL) with meta-learning to ad-dress the challenge of generalization using limited amount of training information. The data used in this paper is lever-aged from different datasets that have diverse distributions, that means distinct feature spaces. We tested our approach on publicly available RS benchmark datasets to perform few-shot RS image classification using meta-learning. The results of the experiments suggest that our approach is able to generalize well on the unseen data even with limited number of training samples and reasonable training time.
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000909743 536__ $$0G:(EU-Grant)956748$$aADMIRE - Adaptive multi-tier intelligent data manager for Exascale (956748)$$c956748$$fH2020-JTI-EuroHPC-2019-1$$x1
000909743 7001_ $$0P:(DE-Juel1)186079$$aRoscher, Ribana$$b1$$ufzj
000909743 7001_ $$0P:(DE-Juel1)132239$$aRiedel, Morris$$b2$$ufzj
000909743 7001_ $$0P:(DE-Juel1)132190$$aMemon, Mohammad Shahbaz$$b3$$ufzj
000909743 7001_ $$0P:(DE-Juel1)171343$$aCavallaro, Gabriele$$b4$$ufzj
000909743 773__ $$a10.1109/IGARSS46834.2022.9884699
000909743 8564_ $$uhttps://juser.fz-juelich.de/record/909743/files/IGARSS2022_Surbhi_Sharma.pdf$$yOpenAccess
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