Hyperspectral and Spectro-Polarimetric Pixel-Level Classification Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@TechReport{ARL-TR-907,
  author =       "Patrick J. Rauss",
  title =        "Hyperspectral and Spectro-Polarimetric Pixel-Level
                 Classification Using Genetic Programming",
  institution =  "Army Research Laboratory",
  publisher =    "Storming Media",
  year =         "2001",
  number =       "ARL-TR-907",
  address =      "Adelphi, MD, USA",
  month =        nov,
  keywords =     "genetic algorithms, genetic programming,
                 classification, level, spectro, polarimetric, pixel,
                 hyperspectral",
  URL =          "http://handle.dtic.mil/100.2/ADA397832",
  URL =          "http://www.dtic.mil/docs/citations/ADA397832",
  URL =          "http://www.amazon.com/Hyperspectral-Spectro-Polarimetric-Pixel-Level-Classification-Programming/dp/142352506X",
  ISBN =         "1-4235-2506-X",
  abstract =     "The objective force will be relying heavily on their
                 sensors to be a combat multiplier to help improve the
                 force's effectiveness and survivability, particularly
                 for reconnaissance, surveillance, and target
                 acquisition missions. Currently, fielded passive sensor
                 systems are generally ineffective against camouflage,
                 concealment, and deception. Their performance is also
                 sensitive to environmental conditions. To meet future
                 needs, several new sensor systems are being developed
                 and evaluated. Two of these new sensors are passive
                 systems that collect additional, measurable
                 characteristics of light: hyperspectral (HS) systems
                 and spectro-polarimetric (SP) systems. To fully take
                 advantage of the information that these systems collect
                 requires new algorithms and techniques. This report
                 discusses why new techniques are necessary and details
                 the development of a computer-assisted design system
                 for the discovery of classification algorithms via a
                 small number of sample target and background
                 signatures. The technique is called genetic programming
                 (GP). GP is an adaptive learning technique that
                 automatically generates a computer program (in this
                 work, a mathematical equation) to solve the problem it
                 is given.

                 This report documents work conducted primarily between
                 September 1999 and August 2000, while the author was on
                 a rotation at the University of Michigan under the
                 Federated Laboratories Consortium program. The report
                 demonstrates that GP could be a useful technique for
                 processing HS and SP data. The experiments reported
                 here show that by using even the simplest of operators
                 (addition, subtraction, multiplication and division)
                 the GP process can develop interesting and potentially
                 useful solution equations. The results shown here are
                 encouraging. However, many questions remain to be
                 answered.",
  notes =        "Accession Number : ADA397832 Final report. Oct
                 1999-Sep 2000.

                 Available as book published by Storming Media",
  size =         "79 pages",
}

Genetic Programming entries for Patrick J Rauss

Citations