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Sensors 2009, 9(7), 5558-5579; doi:10.3390/s90705558

An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network

1,* , 3
1 Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China 2 Graduate School of the Chinese Academy of Science, CAS, Beijing,100039, China 3 National Satellite Meteorological Center, Beijing 100081, China 4 School of Civil Engineering and Environmental Sciences, University of Oklahoma, National Weather Center Suite 3630, Norman, OK 73019, USA
* Author to whom correspondence should be addressed.
Received: 16 June 2009 / Revised: 7 July 2009 / Accepted: 7 July 2009 / Published: 14 July 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
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The crowning objective of this research was to identify a better cloud classification method to upgrade the current window-based clustering algorithm used operationally for China’s first operational geostationary meteorological satellite FengYun-2C (FY-2C) data. First, the capabilities of six widely-used Artificial Neural Network (ANN) methods are analyzed, together with the comparison of two other methods: Principal Component Analysis (PCA) and a Support Vector Machine (SVM), using 2864 cloud samples manually collected by meteorologists in June, July, and August in 2007 from three FY-2C channel (IR1, 10.3-11.3 μm; IR2, 11.5-12.5 μm and WV 6.3-7.6 μm) imagery. The result shows that: (1) ANN approaches, in general, outperformed the PCA and the SVM given sufficient training samples and (2) among the six ANN networks, higher cloud classification accuracy was obtained with the Self-Organizing Map (SOM) and Probabilistic Neural Network (PNN). Second, to compare the ANN methods to the present FY-2C operational algorithm, this study implemented SOM, one of the best ANN network identified from this study, as an automated cloud classification system for the FY-2C multi-channel data. It shows that SOM method has improved the results greatly not only in pixel-level accuracy but also in cloud patch-level classification by more accurately identifying cloud types such as cumulonimbus, cirrus and clouds in high latitude. Findings of this study suggest that the ANN-based classifiers, in particular the SOM, can be potentially used as an improved Automated Cloud Classification Algorithm to upgrade the current window-based clustering method for the FY-2C operational products.
Keywords: FY-2C; multi-channel satellite image; ANN; cloud classification FY-2C; multi-channel satellite image; ANN; cloud classification
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Liu, Y.; Xia, J.; Shi, C.-X.; Hong, Y. An Improved Cloud Classification Algorithm for China’s FY-2C Multi-Channel Images Using Artificial Neural Network. Sensors 2009, 9, 5558-5579.

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