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Proceedings Paper

Neural net classifier for satellite imageries
Author(s): S. L. Hung; Andrew Y. S. Cheng; Victor C.S. Lee
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Paper Abstract

Accurate identification of cloud type is an important aspect of weather forecasting. One of the primary applications of the remotely sensed cloud cover data is to provide synoptic cloud cover information over extensive data-sparse regions; particularly the oceans and deserts. In southeast Asia, information on cloud cover data is obtained from the infrared and visible channels by Geostationary Meteorological Satellite. These imageries contain data of clouds. By extracting the textural features embedded in the images, information on cloud types can be derived and mapped spatially. An artificial neural network is used as a classifier to identify different cloud types through comprehensive training cycles. The architecture of the network used in the present study is multilayered with feedforward and backpropagation. The study makes use of a classification scheme based on the SYNOP code of the World Meteorological Organization (WMO). The average cloud classification accuracy obtained in this study is 40%.

Paper Details

Date Published: 16 September 1992
PDF: 6 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.140005
Show Author Affiliations
S. L. Hung, City Polytechnic of Hong Kong (Hong Kong China)
Andrew Y. S. Cheng, City Polytechnic of Hong Kong (Hong Kong China)
Victor C.S. Lee, City Polytechnic of Hong Kong (Hong Kong China)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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