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Real-time causal processing of anomaly detection for hyperspectral imagery

机译：高光谱图像异常检测的实时因果处理

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摘要

Anomaly detection generally requires real-time processing to find targets on a timely basis. However, for an algorithm to be implemented in real time, the used data samples can be only those up to the data sample being visited; no future data samples should be involved in the data processing. Such a property is generally called causality, which has unfortunately received little interest thus far in real-time hyperspectral data processing. This paper develops causal processing to perform anomaly detection that can be also implemented in real time. The ability of real-time causal processing is derived from the concept of innovations used to derive a Kalman filter via a recursive causal update equation. Specifically, two commonly used anomaly detectors, sample covariance matrix (K)-based Reed-Xiaoli detector (RXD), called K-RXD, and sample correlation matrix (R)-based RXD, called R-RXD, are derived for their real-time causal processing versions. To substantiate their utility in applications of anomaly detection, real image data sets are conducted for experiments.
机译：异常检测通常需要实时处理以及时找到目标。但是，对于要实时实施的算法，所使用的数据样本只能是那些正被访问的数据样本；将来的数据样本不应参与数据处理。这种属性通常称为因果关系，不幸的是，到目前为止，它对实时高光谱数据处理的兴趣不大。本文开发了因果处理来执行异常检测，该处理也可以实时实现。实时因果处理能力源自用于通过递归因果更新方程式推导卡尔曼滤波器的创新概念。具体来说，两个常用的异常检测器是基于它们的实数得出的，它们是基于样本协方差矩阵（K）的Reed-Xiaoli检测器（RXD），称为K-RXD，以及基于样本相关矩阵（R）的RXD，称为R-RXD因果处理版本。为了证实其在异常检测应用中的效用，对真实图像数据集进行了实验。

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