  Hyperspectral target detection has been widely studied in the field of remote
sensing. However, background dictionary building issue and the correlation
analysis of target and background dictionary issue have not been well studied.
To tackle these issues, a \emph{Weighted Hierarchical Sparse Representation}
for hyperspectral target detection is proposed. The main contributions of this
work are listed as follows. 1) Considering the insufficient representation of
the traditional background dictionary building by dual concentric window
structure, a hierarchical background dictionary is built considering the local
and global spectral information simultaneously. 2) To reduce the impureness
impact of background dictionary, target scores from target dictionary and
background dictionary are weighted considered according to the dictionary
quality. Three hyperspectral target detection data sets are utilized to verify
the effectiveness of the proposed method. And the experimental results show a
better performance when compared with the state-of-the-arts.
