Uncertainty analysis of an evolutionary algorithm to develop remote sensing spectral indices
Created by W.Langdon from
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- @InProceedings{Momm:2008:SPIE,
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author = "Henrique G Momm and Greg Easson and Joel Kuszmaul",
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title = "Uncertainty analysis of an evolutionary algorithm to
develop remote sensing spectral indices",
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booktitle = "Image Processing: Algorithms and Systems VI",
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year = "2008",
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editor = "Jaakko T. Astola and Karen O. Egiazarian and
Edward R. Dougherty",
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volume = "6812",
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pages = "68120A.1--68120A.9",
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address = "San Jose, California, USA",
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month = "28 " # jan,
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publisher = "SPIE--The International Society for Optical
Engineering",
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keywords = "genetic algorithms, genetic programming",
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DOI = "DOI:10.1117/12.766367",
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abstract = "The need for information extracted from remotely
sensed data has increased in recent decades. To address
this issue, research is being conducted to develop a
complete multi-stage supervised object recognition
system. The first stage of this system couples genetic
programming with standard unsupervised clustering
algorithms to search for the optimal preprocessing
function. This manuscript addresses the quantification
and the characterisation of the uncertainty involved in
the random creation of the first set of candidate
solutions from which the algorithm begins. We used a
Monte Carlo type simulation involving 800 independent
realisations and then analyzed the distribution of the
final results. Two independent convergence approaches
were investigated: [1] convergence based solely on
genetic operations (standard) and [2] convergence based
on genetic operations with subsequent insertion of new
genetic material (restarting). Results indicate that
the introduction of new genetic material should be
incorporated into the preprocessing framework to
enhance convergence and to reduce variability.",
- }
Genetic Programming entries for
Henrique G Momm
Greg Easson
Joel S Kuszmaul
Citations