@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 = "Yool, {Stephen R.} and M. Brandley and C. Kern and Gerlach, {Francis W.} and Rhodes, {Ken L.}",
year = "1992",
language = "English (US)",
isbn = "0819409391",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "Publ by Int Soc for Optical Engineering",
pages = "497--503",
booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",
note = "Neural and Stochastic Methods in Image and Signal Processing ; Conference date: 20-07-1992 Through 23-07-1992",
}