  The Cherenkov Telescope Array (CTA) is the future observatory for
ground-based imaging atmospheric Cherenkov telescopes. Each telescope will
provide a snapshot of gamma-ray induced particle showers by capturing the
induced Cherenkov emission at ground level. The simulation of such events
provides camera images that can be used as training data for convolutional
neural networks (CNNs) to differentiate signals from background events and to
determine the energy of the initial gamma-ray events. Pattern spectra are
commonly used tools for image classification and provide the distributions of
the sizes and shapes of features comprising an image. The application of
pattern spectra on a CNN allows the selection of relevant combinations of
features within an image. In this work, we generate pattern spectra from
simulated gamma-ray images to train a CNN for signal-background separation and
energy reconstruction for CTA. We compare our results to a CNN trained with CTA
images and find that the pattern spectra-based analysis is computationally less
expensive but not competitive with the purely CTA images-based analysis. Thus,
we conclude that the CNN must rely on additional features in the CTA images not
captured by the pattern spectra.
