基于低秩张量分解的高光谱RX异常目标检测算法

Algorithm of RX anomaly target detection for hyperspectral imagery based on low-rank tensor decomposition

经典的RX异常检测算子假设背景数据信息符合高斯分布,但是由于高光谱图像混有大量的加性噪声,使得图像产生退化,背景信息并不完全符合这类分布。针对这一问题,提出了基于低秩张量分解的高光谱图像RX异常目标检测算法。该方法首先利用高光谱图像的张量数据结构和低秩数据特性,引入低秩张量分解方法对高光谱图像进行数据恢复,使得异常目标信息相比于复杂背景信息变得突出;再利用RX异常检测算子对恢复之后的高光谱图像进行异常目标检测;最后得到异常目标检测结果。通过仿真实验对比,提出的新的异常目标检测方法具有检测精度高、虚警率低和鲁棒性好的特点。

The classical RX anomaly detection operator assumes that the background data information conforms to Gaussian distribution,but the hyperspectral image is degraded due to a large amount of additive noise,and the background information does not conform to this kind of distribution.To solve this problem,the algorithm of RX anomaly target detection for hyperspectral imagery based on low-rank tensor decomposition is proposed.Firstly,the low rank tensor decomposition method is introduced to recover the hyperspectral image,and which uses the tensor data structure and low rank data characteristics of hyperspectral image,so that the anomaly target information becomes prominent compared with the complex background information,and then the RX anomaly detection operator is used to detect the anomaly target in the recovered hyperspectral image;Finally,the anomaly target detection results are obtained.Through the comparison of simulation experiments,the new anomaly target detection method has the characteristics of high detection accuracy,low false alarm rate and good robustness.