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1196 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 5, SEPTEMBER 2001 Brief Papers_______________________________________________________________________________
 

Summary: 1196 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 12, NO. 5, SEPTEMBER 2001
Brief Papers_______________________________________________________________________________
Temporal Updating Scheme for Probabilistic Neural Network With Application
to Satellite Cloud Classification--Further Results
Mahmood R. Azimi-Sadjadi, Wenfeng Gao, Thomas H. Vonder Haar, and Donald Reinke
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 Opera-
tional Environmental Satellite) imagery data. In this brief paper,
a new method referred to as moving singular value decomposi-
tion (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 per-
formance of the temporal updating process by almost 10%.
Index Terms--Cloud classification, maximum likelihood, prob-
abilistic neural networks (PNNs), singular value decomposition

  

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

 

Collections: Computer Technologies and Information Sciences