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@ARTICLE{Zhao:909065,
      author       = {Zhao, Bin and Ragnarsson, Haukur Isfeld and Ulfarsson,
                      Magnus O. and Cavallaro, Gabriele and Benediktsson, Jon
                      Atli},
      title        = {{P}redicting {C}lassification {P}erformance for {B}enchmark
                      {H}yperspectral {D}atasets},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {15},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-02983},
      pages        = {4180 - 4193},
      year         = {2022},
      abstract     = {The classification of hyperspectral images (HSIs) is an
                      essential application of remote sensing and it is addressed
                      by numerous publications every year. A large body of these
                      papers present new classification algorithms and benchmark
                      them against established methods on public hyperspectral
                      datasets. The metadata contained in these research papers
                      (i.e., the size of the image, the number of classes, the
                      type of classifier, etc.) present an unexploited source of
                      information that can be used to estimate the performance of
                      classifiers before doing the actual experiments. In this
                      article, we propose a novel approach to investigate to what
                      degree HSIs can be classified by using only metadata. This
                      can guide remote sensing researchers to identify optimal
                      classifiers and develop new algorithms. In the experiments,
                      different linear and nonlinear prediction methods are
                      trained and tested by using data on classification accuracy
                      and metadata from 100 HSIs classification papers. The
                      experimental results demonstrate that the proposed ensemble
                      learning voting method outperforms other comparative methods
                      in quantitative assessments.},
      cin          = {JSC},
      ddc          = {520},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000805801800006},
      doi          = {10.1109/JSTARS.2022.3173893},
      url          = {https://juser.fz-juelich.de/record/909065},
}