  We briefly review recent progress in techniques for modeling and analyzing
hyperspectral images and movies, in particular for detecting plumes of both
known and unknown chemicals. For detecting chemicals of known spectrum, we
extend the technique of using a single subspace for modeling the background to
a "mixture of subspaces" model to tackle more complicated background.
Furthermore, we use partial least squares regression on a resampled training
set to boost performance. For the detection of unknown chemicals we view the
problem as an anomaly detection problem, and use novel estimators with
low-sampled complexity for intrinsically low-dimensional data in
high-dimensions that enable us to model the "normal" spectra and detect
anomalies. We apply these algorithms to benchmark data sets made available by
the Automated Target Detection program co-funded by NSF, DTRA and NGA, and
compare, when applicable, to current state-of-the-art algorithms, with
favorable results.
