{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:49:11Z","timestamp":1776181751777,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2009,7,14]],"date-time":"2009-07-14T00:00:00Z","timestamp":1247529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019s 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 \u03bcm; IR2, 11.5-12.5 \u03bcm and WV 6.3-7.6 \u03bcm) 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.<\/jats:p>","DOI":"10.3390\/s90705558","type":"journal-article","created":{"date-parts":[[2009,7,14]],"date-time":"2009-07-14T12:38:04Z","timestamp":1247575084000},"page":"5558-5579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["An Improved Cloud Classification Algorithm for China\u2019s FY-2C Multi-Channel Images Using Artificial Neural Network"],"prefix":"10.3390","volume":"9","author":[{"given":"Yu","family":"Liu","sequence":"first","affiliation":[{"name":"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"},{"name":"Graduate School of the Chinese Academy of Science, CAS, Beijing,100039, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xia","sequence":"additional","affiliation":[{"name":"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"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Xiang","family":"Shi","sequence":"additional","affiliation":[{"name":"National Satellite Meteorological Center, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Environmental Sciences, University of Oklahoma, National Weather Center Suite 3630, Norman, OK 73019, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2009,7,14]]},"reference":[{"key":"ref_1","unstructured":"Hobbs, P.V., and Deepak, A. 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