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A temporally adaptive classifier for multispectral imagery

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3 Author(s)
Jianqi Wang ; Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA ; Azimi-Sadjadi, M.R. ; Reinke, D.

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:15 ,  Issue: 1 )