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@INPROCEEDINGS{Patnala:1025761,
      author       = {Patnala, Ankit and Stadtler, Scarlet and Schultz, Martin G.
                      and Gall, Juergen},
      title        = {{M}ulti-{M}odal {S}elf-{S}upervised {L}earning for
                      {B}oosting {C}rop {C}lassification {U}sing {S}entinel2 and
                      {P}lanetscope},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-03129},
      pages        = {2223 - 2226},
      year         = {2023},
      note         = {ISBN: 979-8-3503-2010-7},
      comment      = {IGARSS 2023 - 2023 IEEE International Geoscience and Remote
                      Sensing Symposium},
      booktitle     = {IGARSS 2023 - 2023 IEEE International
                       Geoscience and Remote Sensing
                       Symposium},
      abstract     = {Remote sensing has enabled large-scale crop classification
                      to understand agricultural ecosystems and estimate
                      production yields. Since few years, machine learning is
                      increasingly used for automated crop classification.
                      However, most approaches apply novel algorithms to custom
                      datasets containing information of few crop fields covering
                      a small region and this often leads to poor models that lack
                      generalization capability. Therefore in this work, inspired
                      from the self-supervised learning approaches, we devised and
                      compared different approaches for contrastive
                      self-supervised learning using Sentinel2 and Planetscope
                      data for crop classification. In addition, based on the
                      dataset DENETHOR, we assembled our own dataset for the
                      experiments.},
      month         = {Jul},
      date          = {2023-07-16},
      organization  = {IGARSS 2023 - 2023 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Pasadena (CA), 16 Jul 2023 -
                       21 Jul 2023},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001098971602119},
      doi          = {10.1109/IGARSS52108.2023.10282665},
      url          = {https://juser.fz-juelich.de/record/1025761},
}