Proceedings of the KSRS Conference (대한원격탐사학회:학술대회논문집)
- 2005.10a
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- Pages.34-37
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- 2005
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- 1226-9743(pISSN)
APPLICATION OF NEURAL NETWORK FOR THE CLOUD DETECTION FROM GEOSTATIONARY SATELLITE DATA
- Ahn, Hyun-Jeong (Remote sensing lab, Meteorological research institute) ;
- Ahn, Myung-Hwan (Remote sensing lab, Meteorological research institute) ;
- Chung, Chu-Yong (Remote sensing lab, Meteorological research institute)
- Published : 2005.10.01
Abstract
An efficient and robust neural network-based scheme is introduced in this paper to perform automatic cloud detection. Unlike many existing cloud detection schemes which use thresholding and statistical methods, we used the artificial neural network methods, the multi-layer perceptrons (MLP) with back-propagation algorithm and radial basis function (RBF) networks for cloud detection from Geostationary satellite images. We have used a simple scene (a mixed scene containing only cloud and clear sky). The main results show that the neural networks are able to handle complex atmospheric and meteorological phenomena. The experimental results show that two methods performed well, obtaining a classification accuracy reaching over 90 percent. Moreover, the RBF model is the most effective method for the cloud classification.
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