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:1.8081
- @InProceedings{Momm:2007:ASPRS,
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author = "Henrique G. Momm and Joel S. Kuszmaul and
Greg Easson",
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title = "Integration of Logistic Regression and Genetic
Programming to Model Coastal Louisiana Land Loss Using
Remote Sensing",
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booktitle = "Proceedings of the ASPRS 2007 Annual Conference",
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year = "2007",
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editor = "Gary Florence",
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address = "Tampa, Florida, USA",
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month = "7-11 " # may,
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organization = "American Society for Photogrammetry and Remote
Sensing",
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keywords = "genetic algorithms, genetic programming, remote
sensing, logistic regression",
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URL = "http://www.asprs.org/a/publications/proceedings/tampa2007/0044.pdf",
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size = "8 pages",
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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.",
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notes = "http://www.asprs.org/conference-archive/tampa2007/",
- }
Genetic Programming entries for
Henrique G Momm
Joel S Kuszmaul
Greg Easson
Citations