A support vector method for anomaly detection in hyperspectral imagery
A support vector method for anomaly detection in hyperspectral imagery
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DOI：
10.1109/tgrs.2006.873019
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发表时间：
2006-08-01
影响因子：
8.2
通讯作者：
Diehl, Chris
中科院分区：
文献类型：
--
作者：
Banerjee, Amit;Burlina, Philippe;Diehl, Chris
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。传统的异常检测算法是基于流行的Reed-Xiaoli检测器。然而，这些算法通常遭受大量的假警报，由于假设本地背景是高斯和均匀的。在实践中，这些假设经常被违反，特别是当像素的邻近区域包含多种类型的地形时。为了消除这些假设，一种新的异常检测器，它结合了6非参数背景模型的基础上SVDD的。扩展以前的SVDD工作，几何解释的SVDD被用来提出一个决策规则，利用一个新的测试统计量和股票的一些属性的恒定误报率检测器。使用接收器工作特性曲线，作者报告的结果表明，使用基于SVDD的探测器进行广域机载地雷探测（WAAMD）和高光谱数字图像收集实验（HYDICE）图像时，性能得到改善，误报率降低。
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 6 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.