A Hierarchical Model for Morphological Galaxy Classification

We propose a new method for the morphological galaxy classification which incorporates two main contributions: (i) the generation of artificial images of galaxies through geometric transformations to be used as additional examples in the training phase, (ii) the use of a novel hierarchical classifier for hierarchical galaxy classification. An additional classifier distinguishes galaxies from stars based on geometrical moments. The proposed method was tested with two different astronomical databases. The results found show that the hierarchical classification method has a higher performance than flat classification, and that the use of artificial examples and oversampling provide a significant improvement in performance.