Anzeige
Erschienen in:
2013 | OriginalPaper | Buchkapitel
70. Ground Nephogram Classification Based on Gabor Wavelet and SVM
verfasst von : Hanguang Li, Qi Wang
Erschienen in: Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012
Verlag: Springer London
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Techniques called feature extraction and pattern recognition are implemented for nephogram representation and recognition. In this approach, the Gabor wavelets are used to extract features of ground nephograms. The principal component analysis (PCA) is applied on the Gabor transformed matrices to reduce the dimensions of these features. Finally by using these features, we can train support vector machine (SVM) classifiers for classifying cloud types. After finishing training, we can use these trained SVM classifiers to classify these cloud types. During these experiments, we used different Kernels and parameters for comparison. Experiment results indicated that the solution proposed in this paper was able to get a good accurate rate which was higher than the solution using Haar-like features and AdaBoost algorithm.