Automated classification techniques of galaxies using artificial neural networks based classifiers

Processing and classifying galaxy information is one of the most important challenges and intensive research area for astronomers. In this paper; analyzing and classifying photographic images of galaxies are presented, with interesting scientific findings gleaned from the processed photographic data. In addition, the performance of ten artificial neural networks (ANNs) based classifiers was evaluated, based on a selected set of features. They are a combination of a set of morphic features; derived from image analysis and principal component analysis (PCA) features. These features are combined and arranged to constitute five groups of features. The results showed that; the support vector machine (SVM) based classifier provides the best results; about 99.529% for a feature set composed of the nine morphic features and 24 principal components; occupying 85% of the original data. The dataset was ten cases of NGC category taken from standardized catalog from Zsolt Frei website.