  Given a target prior information, our goal is to propose a method for
automatically separating targets of interests from the background in
hyperspectral imagery. More precisely, we regard the given hyperspectral image
(HSI) as being made up of the sum of low-rank background HSI and a sparse
target HSI that contains the targets based on a pre-learned target dictionary
constructed from some online spectral libraries. Based on the proposed method,
two strategies are briefly outlined and evaluated to realize the target
detection on both synthetic and real experiments.
