期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 53, 期 1, 页码 679-691出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2022.3175771
关键词
Tensors; Hyperspectral imaging; Anomaly detection; Dictionaries; Principal component analysis; Matrix decomposition; Sparse matrices; Alternating direction method of multipliers (ADMMs); dictionary construction; hyperspectral anomaly detection; sparse representation; tensor low-rank
In hyperspectral anomaly detection, traditional low-rank representation methods may destroy the 3D structure properties of the data. Therefore, we propose a novel tensor low-rank and sparse representation method that can better preserve the multiple subspace properties of the data.
Recently, low-rank representation (LRR) methods have been widely applied for hyperspectral anomaly detection, due to their potentials in separating the backgrounds and anomalies. However, existing LRR models generally convert 3-D hyperspectral images (HSIs) into 2-D matrices, inevitably leading to the destruction of intrinsic 3-D structure properties in HSIs. To this end, we propose a novel tensor low-rank and sparse representation (TLRSR) method for hyperspectral anomaly detection. A 3-D TLR model is expanded to separate the LR background part represented by a tensorial background dictionary and corresponding coefficients. This representation characterizes the multiple subspace property of the complex LR background. Based on the weighted tensor nuclear norm and the $L_{F,1}$ sparse norm, a dictionary is designed to make its atoms more relevant to the background. Moreover, a principal component analysis (PCA) method can be assigned as one preprocessing step to exact a subset of HSI bands, retaining enough the HSI object information and reducing computational time of the postprocessing tensorial operations. The proposed model is efficiently solved by the well-designed alternating direction method of multipliers (ADMMs). A comparison with the existing algorithms via experiments establishes the competitiveness of the proposed method with the state-of-the-art competitors in the hyperspectral anomaly detection task.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据