Hyperspectral Target Detection Based on Low-Rank Background Subspace
  Learning and Graph Laplacian Regularization
Dunbin Shen Xiaorui Ma Wenfeng Kong Jiacheng Tian Hongyu Wang
Hyperspectral target detection is good at finding dim and small objects based
on spectral characteristics. However, existing representation-based methods are
hindered by the problem of the unknown background dictionary and insufficient
utilization of spatial information. To address these issues, this paper
proposes an efficient optimizing approach based on low-rank representation
(LRR) and graph Laplacian regularization (GLR). Firstly, to obtain a complete
and pure background dictionary, we propose a LRR-based background subspace
learning method by jointly mining the low-dimensional structure of all pixels.
Secondly, to fully exploit local spatial relationships and capture the
underlying geometric structure, a local region-based GLR is employed to
estimate the coefficients. Finally, the desired detection map is generated by
computing the ratio of representation errors from binary hypothesis testing.
The experiments conducted on two benchmark datasets validate the effectiveness
and superiority of the approach. For reproduction, the accompanying code is
available at https://github.com/shendb2022/LRBSL-GLR.