  Classifying the morphologies of radio galaxies is important to understand
their physical properties and evolutionary histories. A galaxy's morphology is
often determined by visual inspection, but as survey size increases robust
automated techniques will be needed. Deep neural networks are an attractive
method for automated classification, but have many free parameters and
therefore require extensive training data and are subject to overfitting and
generalization issues. We explore hybrid classification methods using the
scattering transform, the recursive wavelet decomposition of an input image. We
analyse the performance of the scattering transform for the Fanaroff-Riley
classification of radio galaxies with respect to CNNs and other machine
learning algorithms. We test the robustness of the different classification
methods with training data truncation and noise injection, and find that the
scattering transform can offer competitive performance with the most accurate
CNNs.
