Evolutionary Computation for Information Extraction from Remotely Sensed Imagery
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{Momm:thesis,
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author = "Henrique Garcia Momm",
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title = "Evolutionary Computation for Information Extraction
from Remotely Sensed Imagery",
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school = "Department of Geology and Geological Engineering, The
University of Mississippi",
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year = "2008",
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month = may,
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address = "USA",
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keywords = "genetic algorithms, genetic programming, remote
sensing, Applied science, Computer Science, Disaster
management, Earth Science, Evolutionary Computation,
Geotechnology, Image Processing, Information
Extraction",
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URL = "http://search.proquest.com/docview/304514577",
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size = "196 pages",
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abstract = "Automated and semi-automated techniques have been
researched as an alternative way to reduce human
interaction and thus improve the information extraction
process from imagery. This research developed an
innovative methodology by integrating machine learning
algorithms with image processing and remote sensing
procedures to form the evolutionary framework . In this
biologically-inspired methodology, non-linear solutions
are developed by iteratively updating a set of
candidate solutions through operations such as:
reproduction, competition, and selection. Uncertainty
analysis is conducted to quantitatively assess the
system's variability due to the random generation of
the initial set of candidate solutions, from which the
algorithm begins. A new convergence approach is
proposed and results indicate that it not only reduces
the overall variability of the system but also the
number of iterations needed to obtain the optimal
solution. Additionally, the evolutionary framework is
evaluated in solving different remote sensing problems,
such as: non-linear inverse modelling, integration of
image texture with spectral information, and
multitemporal feature extraction. The investigations in
this research revealed that the use of evolutionary
computation to solve remote sensing problems is
feasible. Results also indicate that, the evolutionary
framework reduces the overall dimensionality of the
data by removing redundant information while generating
robust solutions regardless of the variations in the
statistics and the distribution of the data. Thus,
signifying that the proposed framework is capable of
mathematically incorporating the non-linear
relationship between features into the final
solution.",
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notes = "UMI number 3361190",
- }
Genetic Programming entries for
Henrique G Momm
Citations