  Localizing targets of interest in a given hyperspectral (HS) image has
applications ranging from remote sensing to surveillance. This task of target
detection leverages the fact that each material/object possesses its own
characteristic spectral response, depending upon its composition. As
$\textit{signatures}$ of different materials are often correlated, matched
filtering based approaches may not be appropriate in this case. In this work,
we present a technique to localize targets of interest based on their spectral
signatures. We also present the corresponding recovery guarantees, leveraging
our recent theoretical results. To this end, we model a HS image as a
superposition of a low-rank component and a dictionary sparse component,
wherein the dictionary consists of the $\textit{a priori}$ known characteristic
spectral responses of the target we wish to localize. Finally, we analyze the
performance of the proposed approach via experimental validation on real HS
data for a classification task, and compare it with related techniques.
