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@ARTICLE{Canty:17745,
      author       = {Canty, M. J. and Nielsen, A.A.},
      title        = {{L}inear and {K}ernel {M}ethods for {M}ultivariate {C}hange
                      {D}etection},
      journal      = {Computers $\&$ geosciences},
      volume       = {38},
      issn         = {0098-3004},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {PreJuSER-17745},
      pages        = {107 - 114},
      year         = {2012},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {The iteratively reweighted multivariate alteration
                      detection (IR-MAD) algorithm may be used both for
                      unsupervised change detection in multi- and hyperspectral
                      remote sensing imagery and for automatic radiometric
                      normalization of multitemporal image sequences. Principal
                      components analysis (PCA), as well as maximum
                      autocorrelation factor (MAF) and minimum noise fraction
                      (MNF) analyses of IR-MAD images, both linear and
                      kernel-based (nonlinear), may further enhance change signals
                      relative to no-change background. IDL (Interactive Data
                      Language) implementations of IR-MAD, automatic radiometric
                      normalization, and kernel PCA/MAF/MNF transformations are
                      presented that function as transparent and fully integrated
                      extensions of the ENVI remote sensing image analysis
                      environment. The train/test approach to kernel PCA is
                      evaluated against a Hebbian learning procedure. Matlab code
                      is also available that allows fast data exploration and
                      experimentation with smaller datasets. New, multiresolution
                      versions of IR-MAD that accelerate convergence and that
                      further reduce no-change background noise are introduced.
                      Computationally expensive matrix diagonalization and kernel
                      image projections are programmed to run on massively
                      parallel CUDA-enabled graphics processors, when available,
                      giving an order of magnitude enhancement in computational
                      speed. The software is available from the authors' Web
                      sites. (C) 2011 Elsevier Ltd. All rights reserved.},
      keywords     = {J (WoSType)},
      cin          = {IBG-3},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IBG-3-20101118},
      pnm          = {Terrestrische Umwelt},
      pid          = {G:(DE-Juel1)FUEK407},
      shelfmark    = {Computer Science, Interdisciplinary Applications /
                      Geosciences, Multidisciplinary},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000298524100012},
      doi          = {10.1016/j.cageo.2011.05.012},
      url          = {https://juser.fz-juelich.de/record/17745},
}