Name
Title | ||
|---|---|---|
An Improved Cloud Classification Algorithm for China's FY-2C Multi-Channel Images Using Artificial Neural Network. |
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 mu m; IR2, 11.5-12.5 mu m and WV 6.3-7.6 mu m) 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. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.3390/s90705558 | SENSORS |
Keywords | Field | DocType |
FY-2C,multi-channel satellite image,ANN,cloud classification | Data mining,Support vector machine,Communication channel,Algorithm,Probabilistic neural network,Engineering,Cluster analysis,Artificial neural network,Principal component analysis,Geostationary orbit,Cloud computing | Journal |
Volume | Issue | ISSN |
9 | 7 | 1424-8220 |
Citations | PageRank | References |
6 | 0.87 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu Liu | 1 | 29 | 5.15 |
| Jun Xia | 2 | 22 | 7.46 |
| Chun-Xiang Shi | 3 | 6 | 1.54 |
| Yang Hong | 4 | 124 | 35.87 |