Temporal updating scheme for probabilistic neural network with application to satellite cloud classification-further results Azimi-Sadjadi, Mahmood R. ; Gao, Wenfeng ; Vonder Haar, Thomas H. ; Reinke, Donald L. "This work was supported by the Department of Defense Center for Geosciences/Atmospheric Research Agreement DAAL01-98-2-0078." 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 Operational Environmental Satellite) imagery data. In this brief paper, a new method referred to as moving singular value decomposition (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 performance of the temporal updating process by almost 10%. Colorado State University. Libraries 2001 text ; image application/pdf ECEmra00007.pdf FACFECEN100487ARTI eng c2001 IEEE
Temporal updating scheme for probabilistic neural network with application to satellite cloud classification-further results
Azimi-Sadjadi, Mahmood R. ; Gao, Wenfeng ; Vonder Haar, Thomas H. ; Reinke, Donald L.
"This work was supported by the Department of Defense Center for Geosciences/Atmospheric Research Agreement DAAL01-98-2-0078."
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 Operational Environmental Satellite) imagery data. In this brief paper, a new method referred to as moving singular value decomposition (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 performance of the temporal updating process by almost 10%.
Colorado State University. Libraries
2001
text ; image
application/pdf
ECEmra00007.pdf
FACFECEN100487ARTI
eng
c2001 IEEE