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PCA BASED CLASSIFICATION OF SINGLE-LAYERED CLOUD TYPES

The paper presents an automatic classification system, which discriminates the different types of single-layered clouds using Principal Component Analysis (PCA) with enhanced accuracy as compared to other techniques. PCA is an image classification technique, which is typically used for face recognition. PCA can be used to identify the image features called principal components. A principal component is a peculiar feature of an image. The approach described in this paper uses this PCA capability for enhancing the accuracy of cloud image analysis. To demonstrate this enhancement, a software classifier system has been developed that incorporates PCA capability for better discrimination of cloud images. 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.