Evolutionary Computation for Information Extraction from Remotely Sensed Imagery

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Henrique Garcia Momm",
  title =        "Evolutionary Computation for Information Extraction
                 from Remotely Sensed Imagery",
  school =       "Department of Geology and Geological Engineering, The
                 University of Mississippi",
  year =         "2008",
  month =        may,
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming, remote
                 sensing, Applied science, Computer Science, Disaster
                 management, Earth Science, Evolutionary Computation,
                 Geotechnology, Image Processing, Information
  URL =          "http://search.proquest.com/docview/304514577",
  URL =          "http://phdtree.org/pdf/25220687-evolutionary-computation-for-information-extraction-from-remotely-sensed-imagery/",
  size =         "196 pages",
  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
  notes =        "UMI number 3361190",

Genetic Programming entries for Henrique G Momm