High Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies

We discuss recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. We discuss novel techniques for robust modeling of the background in a hyperspectral scene, and for detecting chemicals of known spectrum, 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 use to model the "normal" spectra and detect anomalies. We apply these algorithms to benchmark data sets made available by Lincoln Labs at the Automated Target Detection program co-funded by NSF, DTRA and NGA, and compare, when applicable, to current state-of-art algorithms, with favorable results.