目前,高光谱异常检测的典型方法有:Reed和Xiaoli Yu提出的RX方法[1]和基于光谱混合模型的低概率检测(Low Probability Detection,LPD)方法[2]。
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low probability-of-detection 和低探测概率 ; 低探测概率
low detection probability 低检测概率
low probability of detection 低概率的检测
In this paper,we use low probability detection to fuse the data in feature level;then segment the image and detect anomaly elements. The result eliminates much false alarm and improves the detectability.
提出的异常检测算法是利用低概率检测算法对高光谱数据先进行特征层融合 ,再进行分割、提取异常点 ,其结果降低了虚警和漏警。
参考来源 - 基于特征层融合的高光谱图像异常检测算法研究·2,447,543篇论文数据，部分数据来源于NoteExpress
For the problem of passive location under low detection probability, a multi-sensor fusion tracking algorithm based on sliding window batch technique is presented.
针对低检测概率下的无源定位问题，提出一种基于滑窗批处理的多传感器融合跟踪算法。
The detection probability calculating method about low pulse repetition frequency(LPRF) PD radar against air vehicle is studied.
提出了低脉冲重复频率（LPRF）PD雷达探测飞行器目标的发现概率计算方法。
It offers low false alarm rates and nuisance alarm rates without compromising on high probability of detection levels.
它降低了虚假警报率和干扰警报率，但不会降低高概率的探测级别。
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