  The classification of galaxy morphologies is an important step in the
investigation of theories of hierarchical structure formation. While human
expert visual classification remains quite effective and accurate, it cannot
keep up with the massive influx of data from emerging sky surveys. A variety of
approaches have been proposed to classify large numbers of galaxies; these
approaches include crowdsourced visual classification, and automated and
computational methods, such as machine learning methods based on designed
morphology statistics and deep learning. In this work, we develop two novel
galaxy morphology statistics, descent average and descent variance, which can
be efficiently extracted from telescope galaxy images. We further propose
simplified versions of the existing image statistics concentration, asymmetry,
and clumpiness, which have been widely used in the literature of galaxy
morphologies. We utilize the galaxy image data from the Sloan Digital Sky
Survey to demonstrate the effective performance of our proposed image
statistics at accurately detecting spiral and elliptical galaxies when used as
features of a random forest classifier.
