Classification of X-Ray Galaxy Clusters with Morphological Feature and Tree SVM

Since many sky-survey observations were performed, as well as appreciable amount of data were obtained, study on large-scale evolution of our Universe has become a field of interest. In this work, we concentrate on the X-ray astronomical samples from NASA's Chandra observatory, and propose an approach to classify galaxy clusters (GCs) based on their central gas profiles' morphological features. Firstly, the raw images are preprocessed, and the central gas profile are segmented. Then, the Fourier descriptors and wavelet moments are take advantaged to extract the morphological features. Finally, a tree structure classifier using support vector machine (SVM) is trained and aid us to categorize the X-ray astronomical observations. Experiments and applications of our classification method on the real X-ray astronomical samples were demonstrated, and comparison of our approach with the non-tree SVM classifier was also performed, which proved our approach is accurate and efficient.