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@ARTICLE{Cavallaro:256097,
      author       = {Cavallaro, Gabriele and Riedel, Morris and Richerzhagen,
                      Matthias and Benediktsson, Jon Atli and Plaza, Antonio},
      title        = {{O}n {U}nderstanding {B}ig {D}ata {I}mpacts in {R}emotely
                      {S}ensed {I}mage {C}lassification {U}sing {S}upport {V}ector
                      {M}achine {M}ethods},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {99},
      issn         = {2151-1535},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2015-06117},
      pages        = {1 - 13},
      year         = {2015},
      abstract     = {Owing to the recent development of sensor resolutions
                      onboard different Earth observation platforms, remote
                      sensing is an important source of information for mapping
                      and monitoring natural and man-made land covers. Of
                      particular importance is the increasing amounts of available
                      hyperspectral data originating from airborne and satellite
                      sensors such as AVIRIS, HyMap, and Hyperion with very high
                      spectral resolution (i.e., high number of spectral channels)
                      containing rich information for a wide range of
                      applications. A relevant example is the separation of
                      different types of land-cover classes using the data in
                      order to understand, e.g., impacts of natural disasters or
                      changing of city buildings over time. More recently, such
                      increases in the data volume, velocity, and variety of data
                      contributed to the term big data that stand for challenges
                      shared with many other scientific disciplines. On one hand,
                      the amount of available data is increasing in a way that
                      raises the demand for automatic data analysis elements since
                      many of the available data collections are massively
                      underutilized lacking experts for manual investigation. On
                      the other hand, proven statistical methods (e.g.,
                      dimensionality reduction) driven by manual approaches have a
                      significant impact in reducing the amount of big data toward
                      smaller smart data contributing to the more recently used
                      terms data value and veracity (i.e., less noise, lower
                      dimensions that capture the most important information).
                      This paper aims to take stock of which proven statistical
                      data mining methods in remote sensing are used to contribute
                      to smart data analysis processes in the light of possible
                      automation as well as scalable and parallel processing
                      techniques. We focus on parallel support vector machines
                      (SVMs) as one of the best out-of-the-box classification
                      methods.},
      cin          = {JSC},
      ddc          = {520},
      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)16},
      UT           = {WOS:000368904000004},
      doi          = {10.1109/JSTARS.2015.2458855},
      url          = {https://juser.fz-juelich.de/record/256097},
}