DocumentCode :
1368354
Title :
Temporal updating scheme for probabilistic neural network with application to satellite cloud classification
Author :
Tian, Bin ; Azimi-Sadjadi, Mahmood R. ; Vonder Haar, Thomas H. ; Reinke, Donald
Author_Institution :
Dept. of Electr. Eng., Colorado State Univ., Fort Collins, CO, USA
Volume :
11
Issue :
4
fYear :
2000
fDate :
7/1/2000 12:00:00 AM
Firstpage :
903
Lastpage :
920
Abstract :
In 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
Keywords :
Markov processes; atmospheric techniques; clouds; geophysical signal processing; image classification; image sequences; maximum likelihood estimation; neural nets; probability; remote sensing; GOES; Geostationary Operational Environmental Satellite; ML criterion; Markov chain models; PNN classifiers; classifier performance degradation; image sequence; maximum likelihood criterion; probabilistic neural network; satellite cloud classification; satellite imagery; temporal contextual information; temporal image change; temporal updating scheme; Atmosphere; Atmospheric modeling; Clouds; Degradation; Earth; Feature extraction; Maximum likelihood detection; Neural networks; Predictive models; Satellites;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.857771
Filename :
857771
Link To Document :
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