  Atmospheric characterization of directly imaged planets has thus far been
limited to ground-based observations of young, self-luminous, Jovian planets.
Near-term space- and ground- based facilities like \emph{WFIRST} and ELTs will
be able to directly image mature Jovian planets in reflected light, a critical
step in support of future facilities that aim to directly image terrestrial
planets in reflected light (e.g. HabEx, LUVOIR). These future facilities are
considering the use of photometry to classify planets. Here, we investigate the
intricacies of using colors to classify gas-giant planets by analyzing a grid
of 9,120 theoretical reflected light spectra spread across different
metallicities, pressure-temperature profiles, cloud properties, and phase
angles. We determine how correlated these planet parameters are with the colors
in the \emph{WFIRST} photometric bins and other photometric bins proposed in
the literature. Then we outline under what conditions giant planet populations
can be classified using several supervised multivariate classification
algorithms. We find that giant planets imaged in reflected light can be
classified by metallicity with an accuracy of $>$90\% if they are \emph{a
prior} known to not have significant cloud coverage in the visible part of the
atmosphere, and at least 3 filter observations are available. If the presence
of clouds is not known \emph{a priori}, directly imaged planets can be more
accurately classified by their cloud properties, as oppposed to metallicity or
temperature. Furthermore, we are able to distinguish between cloudy and
cloud-free populations with $>$90\% accuracy with 3 filter observations. Our
statistical pipeline is available on GitHub and can be extended to optimize
science yield of future mission concepts.
