A Hyperheuristic Approach for Unsupervised Land-Cover Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Joao Papa Papa and Luciene Patrici Papa and 
                 Danillo Roberto Pereira and Rodrigo Jose Pisani",
  journal =      "IEEE Journal of Selected Topics in Applied Earth
                 Observations and Remote Sensing",
  title =        "A Hyperheuristic Approach for Unsupervised Land-Cover
  year =         "2016",
  volume =       "9",
  number =       "6",
  pages =        "2333--2342",
  abstract =     "Unsupervised land-use/cover classification is of great
                 interest, since it becomes even more difficult to
                 obtain high-quality labelled data. Still considered one
                 of the most used clustering techniques, the well-known
                 k-means plays an important role in the pattern
                 recognition community. Its simple formulation and good
                 results in a number of applications have fostered the
                 development of new variants and methodologies to
                 address the problem of minimizing the distance from
                 each dataset sample to its nearest centroid (mean). In
                 this paper, we present a genetic programming-based
                 hyperheuristic approach to combine different
                 metaheuristic techniques used to enhance k-means
                 effectiveness. The proposed approach is evaluated in
                 four satellite and one radar image showing promising
                 results, while outperforming each individual
                 metaheuristic technique.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/JSTARS.2016.2557584",
  ISSN =         "1939-1404",
  month =        jun,
  notes =        "Also known as \cite{7471415}",

Genetic Programming entries for Joao Paulo Papa Luciene Patrici Papa Danillo Roberto Pereira Rodrigo Jose Pisani