Liu Jun, Hou Zengfu, Li Wei, Tao Ran, Orlando Danilo, Li Hongbin
IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5557-5567. doi: 10.1109/TNNLS.2021.3071026. Epub 2022 Oct 5.
In this article, anomaly detection is considered for hyperspectral imagery in the Gaussian background with an unknown covariance matrix. The anomaly to be detected occupies multiple pixels with an unknown pattern. Two adaptive detectors are proposed based on the generalized likelihood ratio test design procedure and ad hoc modification of it. Surprisingly, it turns out that the two proposed detectors are equivalent. Analytical expressions are derived for the probability of false alarm of the proposed detector, which exhibits a constant false alarm rate against the noise covariance matrix. Numerical examples using simulated data reveal how some system parameters (e.g., the background data size and pixel number) affect the performance of the proposed detector. Experiments are conducted on five real hyperspectral data sets, demonstrating that the proposed detector achieves better detection performance than its counterparts.
在本文中，针对具有未知协方差矩阵的高斯背景下的高光谱图像进行异常检测。待检测的异常占据多个像素，其模式未知。基于广义似然比检验设计过程及其特殊修改，提出了两种自适应检测器。令人惊讶的是，所提出的两种检测器是等效的。推导了所提出检测器的虚警概率的解析表达式，该表达式表明其对噪声协方差矩阵具有恒定的虚警率。使用模拟数据的数值示例揭示了一些系统参数（例如，背景数据大小和像素数量）如何影响所提出检测器的性能。在五个真实高光谱数据集上进行了实验，结果表明所提出的检测器比其他同类检测器具有更好的检测性能。
