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




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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





*

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Abstract 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. View Full-Text

Keywords: FY-2C; multi-channel satellite image; ANN; cloud classification FY-2C; multi-channel satellite image; ANN; cloud classification





Author: Yu Liu 1,2, Jun Xia 1,* , Chun-Xiang Shi 3 and Yang Hong 4

Source: http://mdpi.com/



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