Genetic Programming as a Preprocessing Tool to Aid Multi-Temporal Imagery Classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Momm:2006:ASPRS,
  author =       "Henrique Momm and Greg Easson and Dawn Wilkins",
  title =        "Genetic Programming as a Preprocessing Tool to Aid
                 Multi-Temporal Imagery Classification",
  booktitle =    "Proceedings of the ASPRS 2006 Annual Conference",
  year =         "2006",
  editor =       "Alan Mikuni and George Hepner",
  address =      "Reno, Nevada, USA",
  month =        "15-" # may,
  organization = "American Society for Photogrammetry and Remote
                 Sensing",
  keywords =     "genetic algorithms, genetic programming, remote
                 sensing",
  URL =          "http://www.asprs.org/a/publications/proceedings/reno2006/0101.pdf",
  size =         "11 pages",
  abstract =     "Classification-based applications of remotely sensed
                 data have increased significantly over the years. Very
                 often, these data are gathered from different sources
                 and in different formats causing the classification
                 process to be scene specific. Alternatively, spectral
                 band indices have been developed to emphasise some
                 elements based on spectral characteristics and
                 therefore improving the final classification accuracy.
                 This research applies a multi-disciplinary approach in
                 which genetic programming (GP) and standard
                 unsupervised algorithms are integrated into a single
                 iterative process to develop spectral indices for each
                 element being investigated (such as water, impervious
                 surfaces, dense vegetation, etc). A set of indices
                 formed by mathematical and logical operations of the
                 spectral bands are evolved using genetic operations.
                 The application of non-linear indices enhances the
                 relative spectral difference among the elements
                 investigated improving the clustering capability of the
                 data. The algorithm's ability to generalise provides an
                 alternative to classify multi-temporal data with a
                 single methodology. An example application is given for
                 the water and impervious surface delineation using
                 Landsat MSS, Landsat TM, and Landsat ETM+ imagery.
                 Initial results are comparable to more labour intensive
                 scene-specific supervised classification.",
  notes =        "http://www.asprs.org/conference-archive/reno2006/final-prog.htm",
}

Genetic Programming entries for Henrique G Momm Greg Easson Dawn Wilkins

Citations