Evaluation of the use of spectral and textural information by an evolutionary algorithm for multi-spectral imagery classification

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  author =       "H. G. Momm and Greg Easson and Joel Kuszmaul",
  title =        "Evaluation of the use of spectral and textural
                 information by an evolutionary algorithm for
                 multi-spectral imagery classification",
  journal =      "Computers, Environment and Urban Systems",
  year =         "2009",
  volume =       "33",
  number =       "6",
  pages =        "463--471",
  month =        nov,
  note =         "Spatial Data Mining-Methods and Applications",
  keywords =     "genetic algorithms, genetic programming, Remote
                 sensing, Image texture, Evolutionary computation,
  ISSN =         "0198-9715",
  DOI =          "doi:10.1016/j.compenvurbsys.2009.07.007",
  size =         "9 pages",
  abstract =     "Considerable research has been conducted on automated
                 and semi-automated techniques that incorporate image
                 textural information into the decision process as an
                 alternative to improve the information extraction from
                 images while reducing time and cost. The challenge is
                 the selection of the appropriate texture operators and
                 the parameters to address a specific problem given the
                 large set of available texture operators. In this study
                 we evaluate the optimization characteristic of an
                 evolutionary framework to evolve solutions combining
                 spectral and textural information in non-linear
                 mathematical equations to improve multi-spectral image
                 classification. Twelve convolution-type texture
                 operators were selected and divided into three groups.
                 The application of these texture operators to a
                 multi-spectral satellite image resulted into three new
                 images (one for each of the texture operator groups
                 considered). These images were used to evaluate the
                 classification of features with similar spectral
                 characteristics but with distinct textural pattern.
                 Classification of these images using a standard image
                 classification algorithm with and without the aid of
                 the evolutionary framework have shown that the process
                 aided by the evolutionary framework yield higher
                 accuracy values in two out of three cases. The
                 optimization characteristic of the evolutionary
                 framework indicates its potential use as a data mining
                 engine to reduce image dimensionality as the system
                 improved accuracy values with reduced number of
                 channels. In addition, the evolutionary framework
                 reduces the time needed to develop custom solutions
                 incorporating textural information, especially when the
                 relation between the features being investigated and
                 the image textural information is not fully
  notes =        "Population Size 40 Candidate solutionsQuickBird",

Genetic Programming entries for Henrique G Momm Greg Easson Joel S Kuszmaul