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@INPROCEEDINGS{Cavallaro:276338,
      author       = {Cavallaro, G. and Riedel, Morris and Bodenstein, C. and
                      Glock, P. and Richerzhagen, M. and Goetz, M. and
                      Benediktsson, J. A.},
      title        = {{S}calable developments for big data analytics in remote
                      sensing},
      publisher    = {IEEE},
      reportid     = {FZJ-2015-06798},
      pages        = {1366 - 1369},
      year         = {2015},
      comment      = {2015 IEEE International Geoscience and Remote Sensing
                      Symposium (IGARSS) : [Proceedings] - IEEE, 2015. - ISBN
                      978-1-4799-7929-5},
      booktitle     = {2015 IEEE International Geoscience and
                       Remote Sensing Symposium (IGARSS) :
                       [Proceedings] - IEEE, 2015. - ISBN
                       978-1-4799-7929-5},
      abstract     = {Big Data Analytics methods take advantage of techniques
                      from the fields of data mining, machine learning, or
                      statistics with a focus on analysing large quantities of
                      data (aka ‘big datasets’) with modern technologies. Big
                      data sets appear in remote sensing in the sense of large
                      volumes, but also in the sense of an ever increasing amount
                      of spectral bands (i.e., high-dimensional data). The remote
                      sensing has traditionally used the above described
                      techniques for a wide variety of application such as
                      classification (e.g., land cover analysis using different
                      spectral bands from satellite data), but more recently
                      scalability challenges occur when using traditional (often
                      serial) methods. This paper addresses observed scalability
                      limits when using support vector machines (SVMs) for
                      classification and discusses scalable and parallel
                      developments used in concrete application areas of remote
                      sensing. Different approaches that are based on massively
                      parallel methods are discussed as well as recent
                      developments in parallel methods.},
      month         = {Jul},
      date          = {2015-07-26},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium, Milan
                       (Italy), 26 Jul 2015 - 31 Jul 2015},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512)},
      pid          = {G:(DE-HGF)POF3-512},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1109/IGARSS.2015.7326030},
      url          = {https://juser.fz-juelich.de/record/276338},
}