  In this paper, we propose a method for separating known 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 known targets based on
a pre-learned target dictionary specified by the user. Based on the proposed
method, two strategies are outlined and evaluated independently to realize the
target detection on both synthetic and real experiments.
