  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.
