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@ARTICLE{Cavallaro:840596,
      author       = {Cavallaro, Gabriele and Falco, Nicola and Dalla Mura, Mauro
                      and Benediktsson, Jon Atli},
      title        = {{A}utomatic {A}ttribute {P}rofiles},
      journal      = {IEEE transactions on image processing},
      volume       = {26},
      number       = {4},
      issn         = {1941-0042},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2017-08101},
      pages        = {1859 - 1872},
      year         = {2017},
      abstract     = {Morphological attribute profiles are multilevel
                      decompositions of images obtained with a sequence of
                      transformations performed by connected operators. They have
                      been extensively employed in performing multiscale and
                      region-based analysis in a large number of applications. One
                      main, still unresolved, issue is the selection of filter
                      parameters able to provide representative and non-redundant
                      threshold decomposition of the image. This paper presents a
                      framework for the automatic selection of filter thresholds
                      based on Granulometric Characteristic Functions (GCFs). GCFs
                      describe the way that non-linear morphological filters
                      simplify a scene according to a given measure. Since
                      attribute filters rely on a hierarchical representation of
                      an image (e.g., the Tree of Shapes) for their
                      implementation, GCFs can be efficiently computed by taking
                      advantage of the tree representation. Eventually, the study
                      of the GCFs allows the identification of a meaningful set of
                      thresholds. Therefore, a trial and error approach is not
                      necessary for the threshold selection, automating the
                      process and in turn decreasing the computational time. It is
                      shown that the redundant information is reduced within the
                      resulting profiles (a problem of high occurrence, as regards
                      manual selection). The proposed approach is tested on two
                      real remote sensing data sets, and the classification
                      results are compared with strategies present in the
                      literature.},
      cin          = {JSC},
      ddc          = {004},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / NORTH STATE - Enabling Intelligent GMES
                      Services for Carbon and Water Balance Modeling of Northern
                      Forest Ecosystems (606962)},
      pid          = {G:(DE-HGF)POF3-512 / G:(EU-Grant)606962},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000398976000006},
      doi          = {10.1109/TIP.2017.2664667},
      url          = {https://juser.fz-juelich.de/record/840596},
}