Remote discrimination of clouds using a neural network

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

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

6 Citations (Scopus)

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
Volume1766
ISBN (Print)0819409391
StatePublished - 1992
Externally publishedYes
EventNeural and Stochastic Methods in Image and Signal Processing - San Diego, CA, USA
Duration: Jul 20 1992Jul 23 1992

Other

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

Fingerprint

Maximum likelihood
discrimination
Neural networks
Climate models
climate models
spectral sensitivity
trends

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Condensed Matter Physics

Cite this

Yool, S., Brandley, M., Kern, C., Gerlach, F. W., & Rhodes, K. L. (1992). Remote discrimination of clouds using a neural network. In Proceedings of SPIE - The International Society for Optical Engineering (Vol. 1766, pp. 497-503). Publ by Int Soc for Optical Engineering.

Remote discrimination of clouds using a neural network. / Yool, Stephen; Brandley, M.; Kern, C.; Gerlach, Francis W.; Rhodes, Ken L.

Proceedings of SPIE - The International Society for Optical Engineering. Vol. 1766 Publ by Int Soc for Optical Engineering, 1992. p. 497-503.

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

Yool, S, Brandley, M, Kern, C, Gerlach, FW & Rhodes, KL 1992, Remote discrimination of clouds using a neural network. in Proceedings of SPIE - The International Society for Optical Engineering. vol. 1766, Publ by Int Soc for Optical Engineering, pp. 497-503, Neural and Stochastic Methods in Image and Signal Processing, San Diego, CA, USA, 7/20/92.
Yool S, Brandley M, Kern C, Gerlach FW, Rhodes KL. Remote discrimination of clouds using a neural network. In Proceedings of SPIE - The International Society for Optical Engineering. Vol. 1766. Publ by Int Soc for Optical Engineering. 1992. p. 497-503
Yool, Stephen ; Brandley, M. ; Kern, C. ; Gerlach, Francis W. ; Rhodes, Ken L. / Remote discrimination of clouds using a neural network. Proceedings of SPIE - The International Society for Optical Engineering. Vol. 1766 Publ by Int Soc for Optical Engineering, 1992. pp. 497-503
@inproceedings{499146045bb9450bb5d25ae3c1a6611a,
title = "Remote discrimination of clouds using a neural network",
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.",
author = "Stephen Yool and M. Brandley and C. Kern and Gerlach, {Francis W.} and Rhodes, {Ken L.}",
year = "1992",
language = "English (US)",
isbn = "0819409391",
volume = "1766",
pages = "497--503",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "Publ by Int Soc for Optical Engineering",

}

TY - GEN

T1 - Remote discrimination of clouds using a neural network

AU - Yool, Stephen

AU - Brandley, M.

AU - Kern, C.

AU - Gerlach, Francis W.

AU - Rhodes, Ken L.

PY - 1992

Y1 - 1992

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0026997841&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0026997841&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0026997841

SN - 0819409391

VL - 1766

SP - 497

EP - 503

BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Publ by Int Soc for Optical Engineering

ER -