  A Convolutional Neural Network architecture was used to classify various
isotopes of time-sequenced gamma-ray spectra, a typical output of a radiation
detection system of a type commonly fielded for security or environmental
measurement purposes. A two-dimensional surface (waterfall plot) in time-energy
space is interpreted as a monochromatic image and standard image-based CNN
techniques are applied. This allows for the time-sequenced aspects of features
in the data to be discovered by the network, as opposed to standard algorithms
which arbitrarily time bin the data to satisfy the intuition of a human
spectroscopist. The CNN architecture and results are presented along with a
comparison to conventional techniques. The results of this novel application of
image processing techniques to radiation data will be presented along with a
comparison to more conventional adaptive methods.
