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@ARTICLE{Canty:5090,
      author       = {Canty, M. J.},
      title        = {{B}oosting a {F}ast {N}eural {N}etwork for {S}upervised
                      {L}and {C}over {C}lassification},
      journal      = {Computers $\&$ geosciences},
      volume       = {35},
      issn         = {0098-3004},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-5090},
      pages        = {1080 - 1295},
      year         = {2009},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {It is demonstrated that the use of an ensemble of neural
                      networks for routine land cover classification of
                      multispectral satellite data can lead to a significant
                      improvement in classification accuracy. Specifically, the
                      AdaBoost.M1 algorithm is applied to a sequence of
                      three-layer, feed-forward neural networks. In order to
                      overcome the drawback of long training time for each network
                      in the ensemble, the networks are trained with an efficient
                      Kalman filter algorithm. On the basis of statistical
                      hypothesis tests, classification performance on
                      multispectral imagery is compared with that of maximum
                      likelihood and support vector machine classifiers. Good
                      generalization accuracies are obtained with computation
                      times of the order of I h or less. The algorithms involved
                      are described in detail and a software implementation in the
                      ENVI/IDL image analysis environment is provided. (C) 2009
                      Elsevier Ltd. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {ICG-4},
      ddc          = {550},
      cid          = {I:(DE-Juel1)VDB793},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Computer Science, Interdisciplinary Applications /
                      Geosciences, Multidisciplinary},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000266544700022},
      doi          = {10.1016/j.cageo.2008.07.004},
      url          = {https://juser.fz-juelich.de/record/5090},
}