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

• Published in: Sensors (Basel, Switzerland) Vol. 9; no. 7; pp. 5558 - 5579
• Main Authors: Liu, Yu, Xia, Jun, Shi, Chun-Xiang, Hong, Yang
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.07.2009; Molecular Diversity Preservation International (MDPI)
• Subjects: Algorithms; ANN; Automation; Classification; cloud classification; Clouds; Clustering; FY-2C; Meteorological satellites; multi-channel satellite image; Neural networks; Performance evaluation; Radiation; Sensors; Statistical methods; Support vector machines; Temperature; Beijing China; China; multi-channel satellite image; FY-2C; ANN; cloud classification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s90705558

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.