Abstract

Classification of different types of cloud images is the primary issue used to forecast precipitation and other weather constituents. A PCA based classification system has been presented in this paper to classify the different types of single-layered and multi-layered clouds. Principal Component Analysis (PCA) provides enhanced accuracy in features based image identification and classification as compared to other techniques. PCA is a feature based classification technique that is characteristically used for image recognition. PCA is based on principal features of an image and these features discreetly represent an image. The used approach in this research uses the principal features of an image to identify different cloud image types with better accuracy. A classifier system has also been designed to exhibit this enhancement. The designed system reads features of gray-level images to create an image space. This image space is used for classification of images. In testing phase, a new cloud image is classified by comparing it with the specified image space using the PCA algorithm.

 Keywords: Feature identification, Multi-layered cloud types recognition, principal components, eigenvectors, weather prediction

(P1150729002, 746KB)

BibTex:

@ARTICLE{P1150729002,

AUTHOR = {Imran S. Bajwa and M. Shahid Naweed and M. Nadim Asif  and S. Irfan Hyder},

TITLE = {Feature Based Image Classification by using Principal Component Analysis},

JOURNAL ={ICGST International Journal on Graphics, Vision and Image Processing, GVIP},

}

(P1150729002, 746KB)