Parallel 2D local pattern spectra of invariant moments for galaxy classification

Abstract

In this paper, we explore the possibility to use 2D pattern spectra as suitable feature vectors in galaxy classification tasks. The focus is on separating mergers from projected galaxies in a data set extracted from the Sloan Digital Sky Survey Data Release 7. Local pattern spectra are built in parallel and are based on an object segmentation obtained by filtering a max-tree structure that preserves faint structures. A set of pattern spectra using size and Hu’s and Flusser’s image invariant moments information is computed for every segmented galaxy. The C4.5 tree classifier with bagging gives the best classification result. Mergers and projected galaxies are classified with a precision of about 80%