Automatic Discovery of Classification and Estimation Algorithms for Earth-Observation Satellite Imagery

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

@InProceedings{riolo:1995:adcea,
  author =       "Rick L. Riolo and Mark P. Line",
  title =        "Automatic Discovery of Classification and Estimation
                 Algorithms for Earth-Observation Satellite Imagery",
  booktitle =    "Working Notes for the AAAI Symposium on Genetic
                 Programming",
  year =         "1995",
  editor =       "E. V. Siegel and J. R. Koza",
  pages =        "73--77",
  address =      "MIT, Cambridge, MA, USA",
  publisher_address = "445 Burgess Drive, Menlo Park, CA 94025, USA",
  month =        "10--12 " # nov,
  publisher =    "AAAI",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.aaai.org/Papers/Symposia/Fall/1995/FS-95-01/FS95-01-010.pdf",
  URL =          "http://www.aaai.org/Library/Symposia/Fall/fs95-01.php",
  size =         "5 pages",
  abstract =     "Under NASA's new Earth Observing System (EOS),
                 satellite imagery is expected to arrive back on Earth
                 at rates of gigabytes/day. Techniques for the
                 extraction of useful information from such massive data
                 streams must be efficient and scalable in order to
                 survive in petabyte archive situations, and they must
                 overcome the opacity inherent in the data by
                 classifying or estimating pixels according to
                 user-specified categories such as crop-type or forest
                 health. We are in the process of applying GP to several
                 related satellite remote sensing (RS) classification
                 and estimation problems in such a way as to surmount
                 the usual obstacles to large-scale exploitation of
                 imagery. The fitness functions used for training are
                 based on how well the discovered programs perform on a
                 set of cases from Landsat Thematic Mapper (TM) imagery.
                 Programs are rated on how well they perform on
                 out-of-training-set samples of cases from the same
                 imagery. We have carried out a number of preliminary
                 experiments on a relatively simple binary
                 classification task. Each case is a set of 7 spectral
                 intensity readings for a pixel and an associated ground
                 truth class: 1 for surface water, 0 for none. The GP
                 system very rapidly discovers simple relations that
                 correctly predict 98percent plus for training and
                 testing data sets. The key problem with the results we
                 have observed so far is that the simple solutions
                 rapidly drive out diversity in the population. Several
                 approaches will be taken in further study in order to
                 try to maintain diversity in the population.",
  notes =        "AAAI-95f GP. Part of \cite{siegel:1995:aaai-fgp} {\em
                 Telephone:} 415-328-3123 {\em Fax:} 415-321-4457 {\em
                 email} info@aaai.org {\em URL:} http://www.aaai.org/",
}

Genetic Programming entries for Rick L Riolo Mark P Line

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