Hyperspectral anomaly detection can separate sparse anomalies from the low-rank background component under an unsupervised behavior due to sufficient spectral information. Therefore, hyperspectral image anomaly detection technology has great application potential and value in public security and national defense. Currently, most existing models attempt to detect anomalous targets with a sparsity prior, without further considering the visual saliency of the targets themselves. To tackle this issue, this paper proposes a saliency-guided sparse low-rank tensor approximation model, called SSLR, to detect anomalous targets from hyperspectral remote sensing images in an unsupervised manner. Specifically, we first explore the saliency information of each pixel for regularizing the sparse anomaly matrix. We then suggest a three-directional tensor nuclear norm to obtain a low-rank background to characterize the background component. We solve the SSLR optimization problem by an efficient alternating direction method of multipliers framework. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed SSLR outperforms some state-of-the-art anomaly detection methods.

由于足够的光谱信息，高光谱异常检测可以在无监督行为下将稀疏异常与低秩背景成分分开。因此，高光谱图像异常检测技术在公共安全和国防领域具有巨大的应用潜力和价值。目前，大多数现有模型试图以稀疏先验来检测异常目标，而没有进一步考虑目标本身的视觉显着性。为了解决这个问题，本文提出了一种显着性引导的稀疏低秩张量近似模型（SSLR），以无监督的方式检测高光谱遥感图像中的异常目标。具体来说，我们首先探索每个像素的显着性信息以正则化稀疏异常矩阵。然后，我们建议使用三向张量核范数来获得低秩背景来表征背景分量。我们通过乘子框架的有效交替方向方法来解决 SSLR 优化问题。在基准高光谱数据集上进行的实验表明，所提出的 SSLR 优于一些最先进的异常检测方法。