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@ARTICLE{Sedona:867894,
      author       = {Sedona, Rocco and Cavallaro, Gabriele and Jitsev, Jenia and
                      Strube, Alexandre and Riedel, Morris and Benediktsson, Jón
                      Atli},
      title        = {{R}emote {S}ensing {B}ig {D}ata {C}lassification with
                      {H}igh {P}erformance {D}istributed {D}eep {L}earning},
      journal      = {Remote sensing},
      volume       = {11},
      number       = {24},
      issn         = {2072-4292},
      address      = {Basel},
      publisher    = {MDPI},
      reportid     = {FZJ-2019-06496},
      pages        = {3056 -},
      year         = {2019},
      abstract     = {High-Performance Computing (HPC) has recently been
                      attracting more attention in remote sensing applications due
                      to the challenges posed by the increased amount of open data
                      that are produced daily by Earth Observation (EO) programs.
                      The unique parallel computing environments and programming
                      techniques that are integrated in HPC systems are able to
                      solve large-scale problems such as the training of
                      classification algorithms with large amounts of Remote
                      Sensing (RS) data. This paper shows that the training of
                      state-of-the-art deep Convolutional Neural Networks (CNNs)
                      can be efficiently performed in distributed fashion using
                      parallel implementation techniques on HPC machines
                      containing a large number of Graphics Processing Units
                      (GPUs). The experimental results confirm that distributed
                      training can drastically reduce the amount of time needed to
                      perform full training, resulting in near linear scaling
                      without loss of test accuracy.},
      cin          = {JSC},
      ddc          = {620},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / 511 - Computational Science and Mathematical
                      Methods (POF3-511) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-HGF)POF3-511 /
                      G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000507333400170},
      doi          = {10.3390/rs11243056},
      url          = {https://juser.fz-juelich.de/record/867894},
}