Reinke, Donald L., authorVonder Haar, Thomas H., authorAzimi-Sadjadi, Mahmood R., authorTian, Bin, authorIEEE, publisher2007-01-032007-01-032000Tian, Bin, et al., Temporal Updating Scheme for Probabilistic Neural Network with Application to Satellite Cloud Classification, IEEE Transactions on Neural Networks 11, no. 4 (July 2000): 903-920.http://hdl.handle.net/10217/925In cloud classification from satellite imagery, temporal change in the images is one of the main factors that causes degradation in the classifier performance. In this paper, a novel temporal updating approach is developed for probabilistic neural network (PNN) classifiers that can be used to track temporal changes in a sequence of images. This is done by utilizing the temporal contextual information and adjusting the PNN to adapt to such changes. Whenever a new set of images arrives, an initial classification is first performed using the PNN updated up to the last frame while at the same time, a prediction using Markov chain models is also made based on the classification results of the previous frame. The results of both the old PNN and the predictor are then compared. Depending on the outcome, either a supervised or an unsupervised updating scheme is used to update the PNN classifier. Maximum likelihood (ML) criterion is adopted in both the training and updating schemes. The proposed scheme is examined on both a simulated data set and the Geostationary Operational Environmental Satellite (GOES) 8 satellite cloud imagery data. These results indicate the improvements in the classification accuracy when the proposed scheme is used.born digitalarticleseng©2000 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.maximum likelihoodMarkov chain modelscloud classificationprobabilistic neural networkstemporal updatingText