  The high dimensionality of hyperspectral imaging forces unique challenges in
scope, size and processing requirements. Motivated by the potential for an
in-the-field cell sorting detector, we examine a $\textit{Synechocystis sp.}$
PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or
deplete cultures. We use deep learning techniques to both successfully classify
cells and generate a mask segmenting the cells/condition from the background.
Further, we use the classification accuracy to guide a data-driven, iterative
feature selection method, allowing the design neural networks requiring 90%
fewer input features with little accuracy degradation.
