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Anomaly Detection in Hyperspectral Imagery

机译:高光谱图像中的异常检测

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Anomaly detection presented in this paper does not need any kind of target information. In other words, target information plays no role in anomaly detection. The purpose of our anomaly detection is to locate and search for targets which are generally unknown, but relatively small with low probabilities in an image scene. These anomalous targets cannot be identified by prior knowledge. Two approaches are considered in this paper, the RX algorithm developed by Reed and Yu [1] and a uniform target detector (UTD) derived from the low probability detection (LPD) in Harsanyi's dissertation [2], both of which operate a matched filter form with different matched signals used in the individual approaches. The matched signal used in the RX algorithm is the pixel vector r while the UTD using the unity vector l the matched signal. In addition, they both can be implemented in real-time.
机译:本文中提出的异常检测不需要任何类型的目标信息。换句话说,目标信息在异常检测中不起作用。我们的异常检测的目的是定位和搜索通常未知的目标,但在图像场景中的概率相对较小。这些异常目标不能通过先验知识来识别。本文考虑了两种方法,通过芦苇和yu [1]开发的Rx算法以及来自Harsanyi论文的低概率检测(LPD)的统一目标检测器(UTD),两者都操作了匹配的滤波器具有不同匹配信号的形式,用于各个方法。 RX算法中使用的匹配信号是使用UNICS向量L匹配信号的UTD的像素向量R。此外,它们都可以实时实现。

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