Evolving Retrieval Algorithms with a Genetic Programming Scheme

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

@InProceedings{theiler99evolving,
  author =       "James P. Theiler and Neal R. Harvey and 
                 Steven P. Brumby and John J. Szymanski and Steve Alferink and 
                 Simon J. Perkins and Reid B. Porter and 
                 Jeffrey J. Bloch",
  title =        "Evolving Retrieval Algorithms with a Genetic
                 Programming Scheme",
  booktitle =    "Proceedings of SPIE 3753 Imaging Spectrometry V",
  year =         "1999",
  editor =       "Michael R. Descour and Sylvia S. Shen",
  pages =        "416--425",
  organisation = "SPIE--The International Society for Optical
                 Engineering",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://public.lanl.gov/jt/Papers/ga-spie.ps",
  URL =          "http://citeseer.ist.psu.edu/theiler99evolving.html",
  DOI =          "doi:10.1117/12.366303",
  size =         "10 pages",
  abstract =     "The retrieval of scene properties (surface
                 temperature, material type, vegetation health, etc.)
                 from remotely sensed data is the ultimate goal of many
                 earth observing satellites. The algorithms that have
                 been developed for these retrievals are informed by
                 physical models of how the raw data were generated.
                 This includes models of radiation as emitted and/or
                 rejected by the scene, propagated through the
                 atmosphere, collected by the optics, detected by the
                 sensor, and digitised by the electronics. To some
                 extent, the retrieval is the inverse of this
                 ''forward'' modelling problem. But in contrast to this
                 forward modeling, the practical task of making
                 inferences about the original scene usually requires
                 some ad hoc assumptions, good physical intuition, and a
                 healthy dose of trial and error. The standard MTI data
                 processing pipeline will employ algorithms developed
                 with this traditional approach. But we will discuss
                 some preliminary research on the use of a genetic
                 programming scheme to ''evolve'' retrieval algorithms.
                 Such a scheme cannot compete with the physical
                 intuition of a remote sensing scientist, but it may be
                 able to automate some of the trial and error. In this
                 scenario, a training set is used, which consists of
                 multispectral image data and the associated ''ground
                 truth;'' that is, a registered map of the desired
                 retrieval quantity. The genetic programming scheme
                 attempts to combine a core set of image processing
                 primitives to produce an IDL (Interactive Data
                 Language) program which estimates this retrieval
                 quantity from the raw data.",
  bibsource =    "OAI-PMH server at prodweb.osti.gov",
  oai =          "oai:osti.gov:772989",
  subject =      "59 BASIC BIOLOGICAL SCIENCES; ALGORITHMS; DATA
                 PROCESSING; GENETICS; IMAGE PROCESSING; OPTICS; PLANTS;
                 PROGRAMMING; RADIATIONS; REMOTE SENSING; SATELLITES",
  URL =          "http://www.osti.gov/servlets/purl/772989-pvDHZz/native/",
  notes =        "3753-47 Published: 10/1999 Order this volume
                 http://bookstore.spie.org/cgi-bin/order.acgi?t=a&v=3753&of=1

                 also known as \cite{oai:osti.gov:772989}",
}

Genetic Programming entries for James Theiler Neal R Harvey Steven P Brumby John J Szymanski Steve Alferink Simon Perkins Reid B Porter Jeffrey J Bloch

Citations