Estimating per-pixel thematic uncertainty in remote sensing classifications
International Journal of Remote Sensing
Just in time classifiers: managing the slow drift case
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
An extension of the standard mixture model for image segmentation
IEEE Transactions on Neural Networks
Dirichlet Gaussian mixture model: Application to image segmentation
Image and Vision Computing
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This paper presents a new temporally adaptive classification system for multispectral images. A spatial-temporal adaptation mechanism is devised to account for the changes in the feature space as a result of environmental variations. Classification based upon spatial features is performed using Bayesian framework or probabilistic neural networks (PNNs) while the temporal updating takes place using a spatial-temporal predictor. A simple iterative updating mechanism is also introduced for adjusting the parameters of these systems. The proposed methodology is used to develop a pixel-based cloud classification system. Experimental results on cloud classification from satellite imagery are provided to show the usefulness of this system.