红外技术2026，Vol.48Issue(3)：330-339,10.
基于平滑先验与张量低秩表示的高光谱异常检测
Hyperspectral Anomaly Detection Based on a Smooth Prior and Tensor Low-Rank Representation
摘要
Abstract
Hyperspectral anomaly detection is used to separate spatial spectral feature pixels with evident differences from the background.As traditional matrix-based detection methods convert hyperspectral cube data into matrices,they lose spatial spectral information,and noise can interfere with the detection of abnormal information,thereby affecting the detection rate.Therefore,this study proposes a method based on a tensor low-rank representation that retains the geometric features of hyperspectral data and uses a linear iterative clustering algorithm to obtain a dictionary that fully preserves the background feature information of hyperspectral data.Moreover,based on the global and local similarities of the background tensor,this study introduces weighted low-rank and total variation regularization to suppress noise and redundant information.Finally,an efficient solving algorithm was designed using the alternating direction method of multipliers.The experimental results show that the average detection accuracy of this algorithm on four real-scene datasets was 99.44%,verifying its effectiveness.关键词
高光谱图像/线性迭代聚类/加权核范数/异常检测/交替方向乘子法Key words
hyperspectral image/linear iterative clustering/weighted nuclear norm/anomaly detection/alternating direction method of multipliers分类
信息技术与安全科学引用本文复制引用
杨飞霞,张延龙,马飞..基于平滑先验与张量低秩表示的高光谱异常检测[J].红外技术,2026,48(3):330-339,10.基金项目
辽宁省教育厅基本科研创新发展项目(LJ242410147006) （LJ242410147006）
辽宁省科技厅自然科学基金计划面上项目(2023-MS-314). （2023-MS-314）
