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@INPROCEEDINGS{Cavallaro:172735,
      author       = {Cavallaro, Gabriele and Riedel, Morris and Benediktsson,
                      Jon Atli and Goetz, Markus and Runarsson, Tomas and
                      Jonasson, Kristjan and Lippert, Thomas},
      title        = {{S}mart data analytics methods for remote sensing
                      applications},
      publisher    = {IEEE},
      reportid     = {FZJ-2014-06179},
      pages        = {1405 - 1408},
      year         = {2014},
      comment      = {2014 IEEE Geoscience and Remote Sensing Symposium},
      booktitle     = {2014 IEEE Geoscience and Remote
                       Sensing Symposium},
      abstract     = {The big data analytics approach emerged that can be
                      interpreted as extracting information from large quantities
                      of scientific data in a systematic way. In order to have a
                      more concrete understanding of this term we refer to its
                      refinement as smart data analytics in order to examine large
                      quantities of scientific data to uncover hidden patterns,
                      unknown correlations, or to extract information in cases
                      where there is no exact formula (e.g. known physical laws).
                      Our concrete big data problem is the classification of
                      classes of land cover types in image-based datasets that
                      have been created using remote sensing technologies, because
                      the resolution can be high (i.e. large volumes) and there
                      are various types such as panchromatic or different used
                      bands like red, green, blue, and nearly infrared (i.e. large
                      variety). We investigate various smart data analytics
                      methods that take advantage of machine learning algorithms
                      (i.e. support vector machines) and state-of-the-art
                      parallelization approaches in order to overcome limitations
                      of big data processing using non-scalable serial
                      approaches.},
      month         = {Jul},
      date          = {2014-07-13},
      organization  = {IGARSS 2014 - 2014 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Quebec City (Canada), 13 Jul
                       2014 - 18 Jul 2014},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {412 - Grid Technologies and Infrastructures (POF2-412)},
      pid          = {G:(DE-HGF)POF2-412},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:000349688102037},
      doi          = {10.1109/IGARSS.2014.6946698},
      url          = {https://juser.fz-juelich.de/record/172735},
}