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Proceedings Paper

Neural networks and cloud classification
Author(s): Patrick Walder; Iain MacLaren; Carol Reid
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Paper Abstract

The development of an efficient and accurate automated cloud classification method for use on satellite Images will be of great benefit to operational meteorology and climate studies. We have examined the possible use of neural networks as a classification tool for spectral and textural data extracted from Meteosat images. A large number of back-propagation neural network configurations were run and many were found to be highly effective, outperforming more traditional statistical classifiers. A Kohonen type competitive learning network was also tried, but was found to be considerably less successful on this data set. Some suggestions are made for future development based on the experience gained in this project.

Paper Details

Date Published: 30 December 1994
PDF: 10 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196704
Show Author Affiliations
Patrick Walder, Univ. of Paisley (United Kingdom)
Iain MacLaren, Univ. of Paisley (United Kingdom)
Carol Reid, Univ. of Paisley (United Kingdom)


Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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