Remote discrimination of clouds using a neural network

Stephen R. Yool, M. Brandley, C. Kern, Francis W. Gerlach, Ken L. Rhodes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Cloud classification is a key input to global climate models. Cloud spectra are typically mixed, however, thus difficult to classify using the maximum likelihood rule. In contrast to maximum likelihood, a densely interconnected, trained neural network can form powerful generalizations that distinguish unique statistical trends among otherwise ambiguous spectral response patterns. Accordingly, cloud classification accuracies produced by a neural network can exceed accuracies produced using the maximum likelihood criterion.

Original languageEnglish (US)
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages497-503
Number of pages7
ISBN (Print)0819409391
StatePublished - Dec 1 1992
EventNeural and Stochastic Methods in Image and Signal Processing - San Diego, CA, USA
Duration: Jul 20 1992Jul 23 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1766
ISSN (Print)0277-786X

Other

OtherNeural and Stochastic Methods in Image and Signal Processing
CitySan Diego, CA, USA
Period7/20/927/23/92

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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