  Imaging Atmospheric Cherenkov Telescopes (IACTs) detect very-high-energy
gamma rays from ground level by capturing the Cherenkov light of the induced
particle showers. Convolutional neural networks (CNNs) can be trained on IACT
camera images of such events to differentiate the signal from the background
and to reconstruct the energy of the initial gamma ray. Pattern spectra provide
a 2-dimensional histogram of the sizes and shapes of features comprising an
image and they can be used as an input for a CNN to significantly reduce the
computational power required to train it. In this work, we generate pattern
spectra from simulated gamma-ray and proton images to train a CNN for
signal-background separation and energy reconstruction for the Small-Sized
Telescopes (SSTs) of the Cherenkov Telescope Array (CTA). A comparison of our
results with a CNN directly trained on CTA images shows that the pattern
spectra-based analysis is about a factor of three less computationally
expensive but not able to compete with the performance of an CTA image-based
analysis. Thus, we conclude that the CTA images must be comprised of additional
information not represented by the pattern spectra.
