Evolving Retrieval Algorithms with a Genetic Programming Scheme

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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