Image Classification of Single Layered Cloud Types: A PCA based automated system to classify different types of cloud Images for better and concise forecasting of rain - Couverture souple

Bajwa, Imran Sarwar; Hyder, Irfan

 
9783844328264: Image Classification of Single Layered Cloud Types: A PCA based automated system to classify different types of cloud Images for better and concise forecasting of rain
Couverture souple
ISBN 10 : 3844328262 ISBN 13 : 9783844328264
Editeur : LAP LAMBERT Academic Publishing, 2011

Synopsis

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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Présentation de l'éditeur

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.

Biographie de l'auteur

I am Assistant Professor in Computer Science in The Islamia University of Bahawalpur, Pakistan. I am doing my PhD in Computer Science from University of Birmingham, UK. I also worked in University of Coimbra, Portugal, as a Research Associate. My other research interests are Natural Language Processing and Information Systems.

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Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2011
Langue
anglais
ISBN 10 
3844328262
ISBN 13 
9783844328264
Reliure
Broché
Nombre de pages
80
Coordonnées du fabricant
non disponible
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