Mathematical Morphology: Star/Galaxy Differentiation & Galaxy Morphology Classification

Abstract

We present an application of Mathematical Morphology (MM) for the classification of astronomical objects, both for star/galaxy differentiation and galaxy morphology classification. We demonstrate that, for CCD images, 99.3 ± 3.8% of galaxies can be separated from stars using MM, with 19.4 ± 7.9% of the stars being misclassified. We demonstrate that, for photographic plate images, the number of galaxies correctly separated from the stars can be increased using our MM diffraction spike tool, which allows 51.0 ± 6.0% of the high-brightness galaxies that are inseparable in current techniques to be correctly classified, with only 1.4 ± 0.5% of the high-brightness stars contaminating the population. We demonstrate that elliptical (E) and late-type spiral (Sc-Sd) galaxies can be classified using MM with an accuracy of 91.4 ± 7.8%. It is a method involving fewer 'free parameters' than current techniques, especially automated machine learning algorithms. The limitation of MM galaxy morphology classification based on seeing and distance is also presented. We examine various star/galaxy differentiation and galaxy morphology classification techniques commonly used today, and show that our MM techniques compare very favourably.

Cosmic-Ray Detection Based on Gray-Scale Morphology of Spectroscopic CCD Images

“…The center point of the structuring element slides over the surface of the input image. On each pixel point the output pixel value is the maximum value contained within the structuring element superimposed on the input image (Moore et al 2006).…”

“…Dilation and erosion are the basic operators in mathematical morphology and have become common tools for both image processing and analysis of binary and gray-scale images (Heijmans 1992(Heijmans , 1994(Heijmans , 1995van Droogenbroeck & Buckley 2005;Moore et al 2006;Urbach & Wilkinson 2008).…”

“…An approach was proposed to solve the automated star/galaxy discrimination problem through the use of mathematical morphology (Candeas, Braga-Neto & Carvalho Filho 1996, 1997. Then an application of mathematical morphology for the classification of astronomical objects was presented by Moore, Pimbblet & Drinkwater (2006). Considering the morphology of spectra and cosmic-ray hits, using the method of gray-scale morphology to detect cosmic rays is realizable.…”

Hyperconnected Attribute Filters Based on k-Flat Zones

“…1, which shows an image of the M81 galaxy together with many foreground stars. Star/galaxy classification is an important issue which can be handled well by mathematical morphology [19]. We aim one step further, namely, to remove the stars without distorting the internal detail of the galaxy, as was done in [20] using considerable manual intervention [21].…”

Efficient 2-D Grayscale Morphological Transformations With Arbitrary Flat Structuring Elements

“…are the most fundamental operators in mathematical morphology and have become common tools for both image filtering and analysis [3], [4] of binary and grayscale images, especially since the development of efficient algorithms [5]- [14]. Usually, these efficient algorithms can only be used for binary images [5]- [7], [9], [12], [13], or they are limited to shapes that can (efficiently) be decomposed into a series of linear S.E.s [8], [11], [14].…”