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We present a study on galaxy detection and shape classification usingtopometric clustering algorithms. We first use the DBSCAN algorithm to extract,from CCD frames, groups of adjacent pixels with significant fluxes and we thenapply the DENCLUE algorithm to separate the contributions of overlappingsources. The DENCLUE separation is based on the localization of pattern oflocal maxima, through an iterative algorithm which associates each pixel to theclosest local maximum. Our main classification goal is to take apart ellipticalfrom spiral galaxies. We introduce new sets of features derived from thecomputation of geometrical invariant moments of the pixel group shape and fromthe statistics of the spatial distribution of the DENCLUE local maximapatterns. Ellipticals are characterized by a single group of local maxima,related to the galaxy core, while spiral galaxies have additional ones relatedto segments of spiral arms. We use two different supervised ensembleclassification algorithms, Random Forest, and Gradient Boosting. Using a sampleof ~ 24000 galaxies taken from the Galaxy Zoo 2 main sample with spectroscopicredshifts, and we test our classification against the Galaxy Zoo 2 catalog. Wefind that features extracted from our pipeline give on average an accuracy of ~93%, when testing on a test set with a size of 20% of our full data set, withfeatures deriving from the angular distribution of density attractor ranking atthe top of the discrimination power.

ASTErIsM - Application of topometric clustering algorithms in automatic galaxy detection and classification