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@INPROCEEDINGS{Sharma:909743,
      author       = {Sharma, Surbhi and Roscher, Ribana and Riedel, Morris and
                      Memon, Mohammad Shahbaz and Cavallaro, Gabriele},
      title        = {{I}mproving {G}eneralization for {F}ew-{S}hot {R}emote
                      {S}ensing {C}lassification with {M}eta-{L}earning},
      reportid     = {FZJ-2022-03382},
      isbn         = {978-1-6654-2792-0},
      pages        = {5061-5064},
      year         = {2022},
      abstract     = {In 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.},
      month         = {Jul},
      date          = {2022-07-17},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium, Kuala Lumpur
                       (Malaysia), 17 Jul 2022 - 22 Jul 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / ADMIRE - Adaptive
                      multi-tier intelligent data manager for Exascale (956748)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)956748},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000920916605035},
      doi          = {10.1109/IGARSS46834.2022.9884699},
      url          = {https://juser.fz-juelich.de/record/909743},
}