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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 4, JULY 2000 903 Temporal Updating Scheme for Probabilistic
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 11, NO. 4, JULY 2000 903
Temporal Updating Scheme for Probabilistic
Neural Network with Application to Satellite Cloud
Classification
Bin Tian, Mahmood R. Azimi-Sadjadi, Senior Member, IEEE, Thomas H. Vonder Haar, and Donald Reinke
Abstract--In cloud classification from satellite imagery, tem-
poral 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

  

Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University

 

Collections: Computer Technologies and Information Sciences