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@INPROCEEDINGS{Cavallaro:867900,
      author       = {Cavallaro, Gabriele and Bazi, Yakoub and Melgani, Farid and
                      Riedel, Morris},
      title        = {{M}ulti-{S}cale {C}onvolutional {SVM} {N}etworks for
                      {M}ulti-{C}lass {C}lassification {P}roblems of {R}emote
                      {S}ensing {I}mages},
      publisher    = {IEEE},
      reportid     = {FZJ-2019-06502},
      pages        = {875-878},
      year         = {2019},
      comment      = {[Proceedings] - IEEE, 2019. - ISBN 978-1-5386-9154-0},
      booktitle     = {[Proceedings] - IEEE, 2019. - ISBN
                       978-1-5386-9154-0},
      abstract     = {The classification of land-cover classes in remote sensing
                      images can suit a variety of interdisciplinary applications
                      suchas the interpretation of natural and man-made processes
                      onthe Earth surface. The Convolutional Support Vector
                      Machine (CSVM) network was recently proposed as binary
                      classifier for the detection of objects in Unmanned Aerial
                      Vehicle (UAV) images. The training phase of the CSVM isbased
                      on convolutional layers that learn the kernel weightsvia a
                      set of linear Support Vector Machines (SVMs). Thispaper
                      proposes the Multi-scale Convolutional Support VectorMachine
                      (MCSVM) network, that is an ensemble of CSVMclassifiers
                      which process patches of different spatial sizes andcan deal
                      with multi-class classification problems. The experiments
                      are carried out on the EuroSAT Sentinel-2 dataset andthe
                      results are compared to the one obtained with recent
                      transfer learning approaches based on pre-trained
                      ConvolutionalNeural Networks (CNNs).},
      month         = {Jul},
      date          = {2019-07-28},
      organization  = {IGARSS 2019 - 2019 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Yokohama (Japan), 28 Jul
                       2019 - 2 Aug 2019},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / DEEP-EST - DEEP - Extreme Scale Technologies
                      (754304) / HBP - The Human Brain Project (604102)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)754304 /
                      G:(EU-Grant)604102},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000519270601013},
      doi          = {10.1109/IGARSS.2019.8899831},
      url          = {https://juser.fz-juelich.de/record/867900},
}