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首页> 外文会议>IEEE Conference on Computer Vision and Pattern Recognition Workshops >A Novel Detection Paradigm and Its Comparison to Statistical and Kernel-Based Anomaly Detection Algorithms for Hyperspectral Imagery
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A Novel Detection Paradigm and Its Comparison to Statistical and Kernel-Based Anomaly Detection Algorithms for Hyperspectral Imagery

机译：一种新的检测范例及其与高光谱图像统计和基于核的异常检测算法的比较

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摘要

Detection of anomalous pixels within hyperspectral imagery is frequently used for purposes ranging from the location of invasive plant species to the detection of military targets. The task is unsupervised because no information about target or background spectra is known or assumed. Some of the most commonly used detection algorithms assume a statistical distribution for the background and rate spectral anomalousness based on measures of deviation from the statistical model; but such assumptions can be problematic because hyperspectral data rarely meet them. More recent algorithms have employed data-driven machine learning techniques in order to improve performance. Here we investigate a novel kernel-based method and show that it achieves top detection performance relative to seven other state-of-the-art methods on a commonly tested data set.
机译：高光谱图像中的异常像素的检测经常用于从侵入性植物种类的位置到检测军事目标的目的。任务是无监督的，因为没有关于目标或背景光谱的信息是已知或假设的。一些最常用的检测算法是基于与统计模型的偏差衡量标准的背景和速率光谱异常的统计分布;但是这种假设可能是有问题的，因为高光谱数据很少见到它们。最近的算法采用了数据驱动的机器学习技术，以提高性能。在这里，我们调查基于新的内核的方法，并表明它在普通测试的数据集上相对于七种其他最先进的方法实现了最高的检测性能。

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