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A Discriminative Metric Learning Based Anomaly Detection Method

机译：基于判别度量学习的异常检测方法

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Due to the high spectral resolution, anomaly detection from hyperspectral images provides a new way to locate potential targets in a scene, especially those targets that are spectrally different from the majority of the data set. Conventional Mahalanobis-distance-based anomaly detection methods depend on the background statistics to construct the anomaly detection metric. One of the main problems with these methods is that the Gaussian distribution assumption of the background may not be reasonable. Furthermore, these methods are also susceptible to contamination of the conventional background covariance matrix by anomaly pixels. This paper proposes a new anomaly detection method by effectively exploiting a robust anomaly degree metric for increasing the separability between anomaly pixels and other background pixels, using discriminative information. First, the manifold feature is used so as to divide the pixels into the potential anomaly part and the potential background part. This procedure is called discriminative information learning. A metric learning method is then performed to obtain the robust anomaly degree measurements. Experiments with three hyperspectral data sets reveal that the proposed method outperforms other current anomaly detection methods. The sensitivity of the method to several important parameters is also investigated.
机译：由于光谱分辨率高，因此从高光谱图像中进行异常检测提供了一种新的方法来定位场景中的潜在目标，尤其是那些光谱上与大多数数据集不同的目标。传统的基于马氏距离的异常检测方法依赖于背景统计数据来构造异常检测指标。这些方法的主要问题之一是背景的高斯分布假设可能不合理。此外，这些方法还容易受到异常像素对常规背景协方差矩阵的污染。本文提出了一种新的异常检测方法，该方法利用判别信息有效地利用鲁棒的异常度度量来增加异常像素与其他背景像素之间的可分离性。首先，使用流形特征以便将像素划分为潜在异常部分和潜在背景部分。此过程称为判别信息学习。然后执行度量学习方法以获得鲁棒的异常程度度量。在三个高光谱数据集上进行的实验表明，该方法优于其他当前的异常检测方法。还研究了该方法对几个重要参数的敏感性。

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