Proceedings of the KSRS Conference (대한원격탐사학회:학술대회논문집)
- 2007.10a
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- Pages.286-289
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- 2007
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- 1226-9743(pISSN)
TEST ON REAL-TIME CLOUD DETECTION ALGORITHM USING A NEURAL NETWORK MODEL FOR COMS
- Ahn, Hyun-Jeong (Global Environment System Research Laboratory, National Institute of Meteorological Research, KMA) ;
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Chung, Chu-Yong
(Global Environment System Research Laboratory, National Institute of Meteorological Research, KMA) ;
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Ou, Mi-Lim
(Global Environment System Research Laboratory, National Institute of Meteorological Research, KMA)
- Published : 2007.10.31
Abstract
This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.