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A support vector method for anomaly detection in hyperspectral imagery

机译：高光谱图像异常检测的支持向量法

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摘要

This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery.
机译：本文提出了一种基于支持向量数据描述（SVDD）的高光谱图像异常检测方法，该方法是一种对分布的支持建模的核方法。常规的异常检测算法基于流行的Reed-Xiaoli检测器。然而，由于本地背景是高斯且同质的假设，这些算法通常遭受大量的误报。在实践中，通常会违反这些假设，尤其是当像素邻域包含多种类型的地形时。为了消除这些假设，派生了一种新颖的异常检测器，该检测器结合了基于SVDD的非参数背景模型。在先前SVDD工作的基础上，SVDD的几何解释用于提出一种决策规则，该规则利用新的测试统计信息并共享恒定误报率检测器的某些属性。作者使用接收器的工作特性曲线报告了结果，这些结果证明了在基于SVDD的探测器进行广域机载地雷探测（WAAMD）和高光谱数字影像采集实验（HYDICE）影像时使用改进的性能并降低了误报率。

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