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An automatic classification system is presented, which
discriminates the different types of single- layered clouds using
Principal Component Analysis (PCA) with enhanced accuracy and
provides fast processing speed as compared to other techniques. The
system is first trained by cloud images. In training phase, system
reads major principal features of the different cloud images to
produce an image space. In testing phase, a new cloud image can be
classified by comparing it with the specified image space using the
PCA algorithm. Weather forecasting applications use various pattern
recognition techniques to analyze clouds' information and other
meteorological parameters. Neural Networks is an often-used
methodology for image processing. Some statistical methodologies
like FDA, RBFNN and SVM are also being used for image analysis.
These methodologies require more training time and have limited
accuracy of about 70%. This level of accuracy often degrades
classification of clouds, and hence the accuracy of rain and other
weather predictions is reduced. PCA algorithm provides a more
accurate cloud classification that yield better and concise
forecasting of rain.
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