Zhang Lili, Zhao Chunhui
J Opt Soc Am A Opt Image Sci Vis. 2017 Sep 1;34(9):1585-1594. doi: 10.1364/JOSAA.34.001585.
Recently, some methods exploiting both the spatial and spectral features have drawn increasing attention in hyperspectral anomaly detection (AD) and they perform well. In addition, a tensor decomposition-based (TenB) algorithm treating the hyperspectral dataset as a three-order tensor (two modes for space and one mode for spectra) has been proposed to further improve the performance for AD. In this paper, a method using the sparsity divergence index (SDI) based on tensor decomposition (SDI-TD) is proposed. First, three modes of the hyperspectral dataset are obtained by tensor decomposition. Then, low-rank and sparse matrix decomposition is employed separately along the three modes and three sparse matrices are acquired. Finally, SDIs based on the three sparse matrices along the three modes are obtained, and the final result is generated by using the joint SDI. Experiments tested on the real and synthetic hyperspectral dataset reveal that the proposed SDI-TD performs better than the comparison algorithms.
最近，一些利用空间和光谱特征的方法在高光谱异常检测（AD）中受到越来越多的关注，并且表现良好。此外，还提出了一种基于张量分解（TenB）的算法，将高光谱数据集视为三阶张量（空间有两个模式，光谱有一个模式），以进一步提高AD的性能。本文提出了一种基于张量分解的稀疏散度指数（SDI）方法（SDI-TD）。首先，通过张量分解获得高光谱数据集的三种模式。然后，分别沿三种模式进行低秩和稀疏矩阵分解，得到三个稀疏矩阵。最后，获得沿三种模式基于三个稀疏矩阵的SDI，并使用联合SDI生成最终结果。在真实和合成高光谱数据集上进行的实验表明，所提出的SDI-TD比比较算法表现更好。
