发表机构
Central China Normal University; Academy of Mathematics and Systems Science, Chinese Academy of Sciences; University of Chinese Academy of Sciences（华中师范大学; 中国科学院数学与系统科学研究院; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对高光谱异常检测中计算成本高和异常结构表征受限的问题，提出基于自动异常分组的群稀疏低秩张量分解方法GSAA，并融合光谱-空间信息，在五个真实数据集上取得优越检测性能。
AI 中文摘要
低秩张量建模已成为高光谱异常检测的有效工具。然而，现有方法仍存在计算成本高、表征空间结构异常灵活性有限的问题。为解决这些问题，本文提出一种基于自动异常分组的群稀疏低秩张量分解的高光谱异常检测方法（GSAA）。具体而言，通过对张量因子施加群稀疏性来表征低管秩背景，为直接张量秩正则化提供了一种高效替代方案。在异常建模方面，引入潜在分组图以构建自动异常分组惩罚项，使异常组能够从数据中自适应推断，而非在像素级别预定义。为进一步利用互补的光谱和空间信息，GSAA在两个域中均被应用，所得检测图被融合形成GSAA的光谱-空间版本，称为GSAA-SS。开发了一种具有收敛保证的高效线性化交替方向乘子法算法来求解所得模型。在五个真实高光谱数据集上的实验结果表明，与几种最先进方法相比，所提方法实现了优越的检测性能和具有竞争力的计算效率。
英文摘要
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing methods still suffer from high computational cost and limited flexibility in characterizing spatially structured anomalies. To address these issues, this paper proposes a hyperspectral anomaly detection method based on group sparse low-rank tensor factorization with automatic anomaly grouping (GSAA). Specifically, the low tubal rank background is characterized by imposing group sparsity on tensor factors, which provides an efficient alternative to direct tensor rank regularization. For anomaly modeling, a latent grouping map is introduced to build an automatic anomaly grouping penalty, allowing anomaly groups to be adaptively inferred from the data rather than predefined at the pixel level. To further exploit complementary spectral and spatial information, GSAA is applied in both domains, and the resulting detection maps are fused to form a spectral--spatial version of GSAA, termed GSAA-SS. An efficient linearized alternating direction method of multipliers algorithm with convergence guarantee is developed to solve the resulting model. Experimental results on five real hyperspectral datasets demonstrate that the proposed method achieves superior detection performance and competitive computational efficiency compared with several state-of-the-art methods.
