A temporally adaptive classifier for multispectral imagery Wang, Jianqi ; Azimi-Sadjadi, Mahmood R. ; Reinke, Donald L. "This work was supported by the DoD Center for Geoscience Atmospheric Research at Colorado State University under the Cooperative Agreement (#DAAL01-98-2-0078) with the Army Research Laboratory." 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. Colorado State University. Libraries 2004 text ; image application/pdf ECEmra00010.pdf FACFECEN100490ARTI eng c2004 IEEE
A temporally adaptive classifier for multispectral imagery
Wang, Jianqi ; Azimi-Sadjadi, Mahmood R. ; Reinke, Donald L.
"This work was supported by the DoD Center for Geoscience Atmospheric Research at Colorado State University under the Cooperative Agreement (#DAAL01-98-2-0078) with the Army Research Laboratory."
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.
Colorado State University. Libraries
2004
text ; image
application/pdf
ECEmra00010.pdf
FACFECEN100490ARTI
eng
c2004 IEEE