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Temporal updating scheme for probabilistic neural network with application to satellite cloud classification-further results

Date

2001

Authors

Reinke, Donald L., author
Vonder Haar, Thomas H., author
Gao, Wenfeng, author
Azimi-Sadjadi, Mahmood R., author
IEEE, publisher

Journal Title

Journal ISSN

Volume Title

Abstract

A novel temporal updating approach for probabilistic neural network (PNN) classifiers was developed [1] to account for temporal changes of spectral and temperature features of clouds in the visible and infrared (IR) GOES 8 (Geostationary Operational Environmental Satellite) imagery data. In this brief paper, a new method referred to as moving singular value decomposition (MSVD) is introduced to improve the classification rate of the boundary blocks or blocks containing cloud types with nonuniform texture. The MSVD method is then incorporated into the temporal updating scheme and its effectiveness is demonstrated on several sequences of GOES 8 cloud imagery data. These results indicate that the incorporation of the new MSVD improves the overall performance of the temporal updating process by almost 10%.

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Subject

probabilistic neural networks (PNNs)
maximum likelihood
cloud classification
singular value decomposition (SVD)
temporal updating

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