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@INPROCEEDINGS{Cavallaro:842376,
      author       = {Cavallaro, Gabriele and Mura, Mauro Dalla and Riedel,
                      Morris and Benediktsson, Jon Atli},
      title        = {{T}ree-based supervised feature extraction method based on
                      self-dual attribute profiles},
      publisher    = {IEEE},
      reportid     = {FZJ-2018-00617},
      pages        = {775-778},
      year         = {2017},
      abstract     = {Self-Dual Attribute Profiles (SDAPs) have proven to be an
                      effective method for extracting spatial features able to
                      improve scene classification of remote sensing images with
                      very high spatial resolution. An SDAP is a multilevel
                      decomposition of an image obtained with a sequence of
                      transformations performed by attribute filters over the Tree
                      of Shapes (ToS). One of the main issues with this technique
                      is the identification of the filter thresholds generating a
                      SDAP composed of features that should be relevant for the
                      classification problem. This paper proposes a tree-based
                      supervised feature extraction strategy, which is based on
                      Fisher's linear discriminant analysis relying on the
                      available class information. The exploitation of the ToS
                      structure in the threshold selection procedure allows one to
                      avoid any prior full image filtering, as in other related
                      techniques. Furthermore, the ToS automates and optimizes the
                      whole process by decreasing the computational time and
                      overcoming the conventional selection procedure based on
                      trial and error attempts. The proposed automatic spatial
                      feature extraction technique has been tested in the
                      classification of a very high resolution image proving its
                      effectiveness with respect to a conventional selection
                      strategy.},
      month         = {Jul},
      date          = {2017-07-23},
      organization  = {2017 IEEE International Geoscience and
                       Remote Sensing Symposium, Fort Worth,
                       TX (USA), 23 Jul 2017 - 28 Jul 2017},
      cin          = {JSC},
      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)8},
      doi          = {10.1109/IGARSS.2017.8127067},
      url          = {https://juser.fz-juelich.de/record/842376},
}