Integration of Logistic Regression and Genetic Programming to Model Coastal Louisiana Land Loss Using Remote Sensing

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Momm:2007:ASPRS,
  author =       "Henrique G. Momm and Joel S. Kuszmaul and 
                 Greg Easson",
  title =        "Integration of Logistic Regression and Genetic
                 Programming to Model Coastal Louisiana Land Loss Using
                 Remote Sensing",
  booktitle =    "Proceedings of the ASPRS 2007 Annual Conference",
  year =         "2007",
  editor =       "Gary Florence",
  address =      "Tampa, Florida, USA",
  month =        "7-11 " # may,
  organization = "American Society for Photogrammetry and Remote
                 Sensing",
  keywords =     "genetic algorithms, genetic programming, remote
                 sensing, logistic regression",
  URL =          "http://www.asprs.org/a/publications/proceedings/tampa2007/0044.pdf",
  size =         "8 pages",
  abstract =     "The land loss along the Louisiana Coast has been
                 recognised as a growing problem. Efforts have been
                 concentrated in the creation of a Decision Support
                 System (DSS) to better address the problem in which the
                 correct water delineation from remotely sensed data is
                 a critical part of this project. Two different
                 approaches have been evaluated in previous studies:
                 logistic regression and genetic programming. Herein a
                 third approach is proposed by combining genetic
                 programming with logistic regression. This hybrid
                 approach merges the ability of logistic regression to
                 deal with dichotomous data and to provide quantitative
                 results with the optimisation characteristic of genetic
                 programming to search the entire hypothesis space for
                 the ``most fit'' hypothesis. Genetic programming
                 modifies (using an iterative trial and error process)
                 logistic regression models formed by vegetation indices
                 built from basic function blocks defined in the
                 function set (arithmetic operations) and in the
                 terminal set (vegetation indices and spectral bands).
                 Each candidate model is refined with a stepwise
                 backward elimination using the level of significance
                 associated with Chi-square test of each term and then
                 evaluated based on the fitness function which is
                 defined by: the model's, Kappa statistics and the
                 number of terms in the model. The final output is a
                 two-class (water and non-water) classified image of the
                 most fit model.",
  notes =        "http://www.asprs.org/conference-archive/tampa2007/",
}

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

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