16 December 1992 Remote discrimination of clouds using a neural network
Author Affiliations +
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.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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

Back to Top