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.

翻译：高光谱目标检测善于基于光谱特征发现微弱小目标。然而，现有基于表示的方法受限于未知背景字典和空间信息利用不足的问题。为解决这些问题，本文提出一种基于低秩表示（LRR）和图拉普拉斯正则化（GLR）的高效优化方法。首先，为获取完整纯净的背景字典，通过联合挖掘所有像素的低维结构，提出基于LRR的背景子空间学习方法。其次，为充分利用局部空间关系并捕获潜在几何结构，采用基于局部区域的GLR估计系数。最后，通过计算二元假设检验中表示误差的比值生成最终检测图。在两个基准数据集上的实验验证了该方法的有效性和优越性。为便于复现，配套代码见 https://github.com/shendb2022/LRBSL-GLR。