DocumentCode
1748782
Title
A multi-channel temporally adaptable system for continuous cloud classification from satellite imagery
Author
Azimi-Sadjadi, M.R. ; Wang, J. ; Saitwal, K. ; Reinke, D.
Author_Institution
Dept. of Electr. & Comput. Eng., Colorado State Univ., Fort Collins, CO, USA
Volume
3
fYear
2001
fDate
2001
Firstpage
1625
Abstract
A new multi-spectral scheme for cloud classification from satellite imagery is proposed which involves two temporally adaptable probabilistic neural networks, one for the visible and one for the infrared channels. This system offers the ability to perform continuous updating during the whole day. The results using five classes are provided which show the effectiveness of the proposed scheme
Keywords
climatology; clouds; geophysics computing; image classification; neural nets; IR channels; climatology; cloud classification; probabilistic neural networks; satellite imagery; visible channel; Atmosphere; Clouds; Electronic mail; Estimation theory; Infrared imaging; Neural networks; Pattern recognition; Probability density function; Satellites; Temperature;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.938404
Filename
938404
Link To Document