16 December 1992 Remote discrimination of clouds using a neural network
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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.
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Stephen R. Yool, Stephen R. Yool, M. Brandley, M. Brandley, C. Kern, C. Kern, Frank W. Gerlach, Frank W. Gerlach, Ken L. Rhodes, Ken L. Rhodes, } "Remote discrimination of clouds using a neural network", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130885; https://doi.org/10.1117/12.130885
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