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首页> 外文期刊>Multimedia Tools and Applications >A real-time CFAR thresholding method for target detection in hyperspectral images
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A real-time CFAR thresholding method for target detection in hyperspectral images

机译：用于高光谱图像目标检测的实时CFAR阈值化方法

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摘要

In order to support immediate decision-making in critical circumstances such as military reconnaissance and disaster rescue, real-time onboard implementation of target detection is greatly desired. In this paper, a real-time thresholding method (RT-THRES) is proposed to obtain the constant false alarm rate (CFAR) thresholds for target detection in real-time circumstances. RT-THRES utilizes Gaussian mixture model (GMM) to track and fit the distribution of the target detector's outputs. GMM is an extension to Gaussian probability density function, which could approximate any distribution smoothly. In this method, GMM is utilized to model the detector's output, and then the detection threshold is calculated to achieve a CFAR detection. The conventional GMM's parameter estimation by Expectation-Maximization (EM) requires all data samples in the dataset to be involved during the procedure and the the parameters would be re-estimated when new data samples available. Thus, GMM is difficult to be applied in real-time processing when newly observed data samples coming progressively. To improve GMM's application availability in time-critical circumstance, an optimization strategy is proposed by introducing the Incremental GMM(IGMM) which allows GMM's parameter to be estimated online incrementally. Experiments on real hyperspectral image and synthetic dataset suggest that RT-THRES can track and model the detection outputs' distribution accurately which ensures the accuracy of the calculation of CFAR thresholds. Moreover, by applying the optimization strategy the computational consumption of RT-THRES maintains relatively low.
机译：为了支持在紧急情况下（例如军事侦察和灾难救援）的即时决策，非常需要实时机载实施目标检测。本文提出了一种实时阈值法（RT-THRES），以获取恒定的误报率（CFAR）阈值，用于实时情况下的目标检测。 RT-THRES利用高斯混合模型（GMM）跟踪并拟合目标检测器输出的分布。 GMM是高斯概率密度函数的扩展，可以平滑地近似任何分布。在这种方法中，利用GMM对检测器的输出进行建模，然后计算检测阈值以实现CFAR检测。通过期望最大化（EM）进行的常规GMM参数估计需要在过程中包含数据集中的所有数据样本，并且在有新的数据样本可用时将重新估计参数。因此，当新观察到的数据样本逐渐出现时，很难将GMM应用于实时处理。为了提高时间紧迫情况下GMM应用程序的可用性，通过引入增量GMM（IGMM）提出了一种优化策略，该策略允许在线递增地估计GMM的参数。对真实的高光谱图像和合成数据集进行的实验表明，RT-THRES可以准确跟踪和建模检测输出的分布，从而确保CFAR阈值计算的准确性。此外，通过应用优化策略，RT-THRES的计算消耗保持相对较低。

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  • 来源
    《Multimedia Tools and Applications》 |2017年第13期|15155-15171|共17页
  • 作者

    Zhao Huijie; Lou Chen; Li Na;

  • 作者单位

    Beihang Univ, Sch Instrument Sci & Optoelect Engn, Key Lab Precis Optomechatron Technol, Minist Educ, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Instrument Sci & Optoelect Engn, Key Lab Precis Optomechatron Technol, Minist Educ, Beijing 100191, Peoples R China;

    Beihang Univ, Sch Instrument Sci & Optoelect Engn, Key Lab Precis Optomechatron Technol, Minist Educ, Beijing 100191, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Target detection; CFAR detection; Gaussian mixture model;

    机译：目标检测;CFAR检测;高斯混合模型;

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