Rapid sorting of radio galaxy morphology using Haralick features

TL;DR

This paper presents a fast, automated method for classifying radio galaxy morphology using Haralick texture features and hierarchical clustering, enabling efficient analysis of large astronomical image datasets.

Contribution

The study introduces a novel application of Haralick features combined with HDBSCAN clustering for rapid, invariant classification of radio galaxy images, scalable to large surveys.

Findings

Haralick features effectively describe radio galaxy morphology.

Hierarchical clustering groups sources into meaningful morphological classes.

Method is adaptable to other imaging surveys beyond radio galaxies.

Abstract

We demonstrate the use of Haralick features for the automated classification of radio galaxies. The set of thirteen Haralick features represent an extremely compact non-parametric representation of image texture, and are calculated directly from imagery using the Grey Level Co-occurrence Matrix (GLCM). The GLCM is an encoding of the relationship between the intensity of neighbouring pixels in an image. Using 10,000 sources detected in the first data release of the LOFAR Two-metre Sky Survey (LoTSS), we demonstrate that Haralick features are highly efficient, rotationally invariant descriptors of radio galaxy morphology. After calculating Haralick features for LoTSS sources, we employ the fast density-based hierarchical clustering algorithm HDBSCAN to group radio sources into a sequence of morphological classes, illustrating a simple methodology to classify and label new, unseen galaxies in large samples. By adopting a 'soft' clustering approach, we can assign each galaxy a probability of belonging to a given cluster, allowing for more flexibility in the selection of galaxies according to combinations of morphological characteristics and for easily identifying outliers: those objects with a low probability of belonging to any cluster in the Haralick space. Although our demonstration focuses on radio galaxies, Haralick features can be calculated for any image, making this approach also relevant to large optical imaging galaxy surveys.