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A new comparison of hyperspectral anomaly detection algorithms for real-time applications

机译:实时应用中高光谱异常检测算法的新比较

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Due to the high spectral resolution that remotely sensed hyperspectral images provide, there has been an increasing interest in anomaly detection. The aim of anomaly detection is to stand over pixels whose spectral signature differs significantly from the background spectra. Basically, anomaly detectors mark pixels with a certain score, considering as anomalies those whose scores are higher than a threshold. Receiver Operating Characteristic (ROC) curves have been widely used as an assessment measure in order to compare the performance of different algorithms. ROC curves are graphical plots which illustrate the trade- off between false positive and true positive rates. However, they are limited in order to make deep comparisons due to the fact that they discard relevant factors required in real-time applications such as run times, costs of misclassification and the competence to mark anomalies with high scores. This last fact is fundamental in anomaly detection in order to distinguish them easily from the background without any posterior processing. An extensive set of simulations have been made using different anomaly detection algorithms, comparing their performances and efficiencies using several extra metrics in order to complement ROC curves analysis. Results support our proposal and demonstrate that ROC curves do not provide a good visualization of detection performances for themselves. Moreover, a figure of merit has been proposed in this paper which encompasses in a single global metric all the measures yielded for the proposed additional metrics. Therefore, this figure, named Detection Efficiency (DE), takes into account several crucial types of performance assessment that ROC curves do not consider. Results demonstrate that algorithms with the best detection performances according to ROC curves do not have the highest DE values. Consequently, the recommendation of using extra measures to properly evaluate performances have been supported and justified by the conclusions drawn from the simulations.
机译:由于遥感高光谱图像具有很高的光谱分辨率,因此人们对异常检测越来越感兴趣。异常检测的目的是站在其光谱特征与背景光谱显着不同的像素上方。基本上,异常检测器将具有一定分数的像素标记为异常,将其分数高于阈值的像素视为异常。接收器工作特性(ROC)曲线已被广泛用作评估手段,以比较不同算法的性能。 ROC曲线是图形化的图,说明了假阳性率与真阳性率之间的取舍。但是,由于它们会丢弃实时应用程序所需的相关因素(例如运行时间,错误分类的成本以及以高分标记异常的能力)的事实,因此进行深度比较时,它们受到限制。这最后一个事实是异常检测的基础,以便在不进行任何后处理的情况下轻松将它们与背景区分开。已使用不同的异常检测算法进行了广泛的仿真,并使用几种额外的指标比较了它们的性能和效率,以补充ROC曲线分析。结果支持了我们的建议,并表明ROC曲线本身无法很好地显示检测性能。此外,本文提出了一项绩效指标,该指标在单个全局指标中涵盖了为所建议的其他指标得出的所有指标。因此,这个名为检测效率(DE)的数字考虑了ROC曲线未考虑的几种关键性能评估类型。结果表明,根据ROC曲线具有最佳检测性能的算法没有最高DE值。因此,通过仿真得出的结论支持并证明了使用额外措施正确评估性能的建议。

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