Reinke, Donald L., authorVonder Haar, Thomas H., authorGao, Wenfeng, authorAzimi-Sadjadi, Mahmood R., authorIEEE, publisher2007-01-032007-01-032001Azimi-Sadjadi, Mahmood R., et al., Temporal Updating Scheme for Probabilistic Neural Network with Application to Satellite Cloud Classification--Further Results, IEEE Transactions on Neural Networks 12, no. 5 (September 2001): 1196-1203.http://hdl.handle.net/10217/927A 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%.born digitalarticleseng©2001 IEEE.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.probabilistic neural networks (PNNs)maximum likelihoodcloud classificationsingular value decomposition (SVD)temporal updatingText