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Proceedings Paper

Remote discrimination of clouds using a neural network
Author(s): Stephen R. Yool; M. Brandley; C. Kern; Frank W. Gerlach; Ken L. Rhodes
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Paper 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.

Paper Details

Date Published: 16 December 1992
PDF: 7 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130885
Show Author Affiliations
Stephen R. Yool, Lockheed Missiles & Space Co., Inc. (United States)
M. Brandley, Lockheed Missiles & Space Co., Inc. (United States)
C. Kern, Lockheed Missiles & Space Co., Inc. (United States)
Frank W. Gerlach, Lockheed Missiles & Space Co., Inc. (United States)
Ken L. Rhodes, Lockheed Missiles & Space Co., Inc. (United States)


Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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