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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection
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Comparison of the Rayleigh and K-Distributions for Application in Incoherent Change Detection

机译：瑞利和K分布在非相干变化检测中的应用比较

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摘要

The aim of this letter is to compare two incoherent change-detection algorithms for target detection in low-frequency ultrawideband (UWB) synthetic aperture radar (SAR) images. The considered UWB SAR operates in the frequency range from 20 to 90 MHz. Both approaches employ a likelihood ratio test according to the Neyman-Pearson criterion. First, the bivariate Rayleigh probability distribution is used to implement the likelihood ratio test function. This distribution is well known and has been used for change-detection algorithms in low-frequency UWB SAR with good results. Aiming to minimize the false alarm rate and taking into consideration that low-frequency UWB SAR images have high resolution compared to the transmitted wavelength, the second approach implements the test by using a bivariate K-distribution. This distribution has scale and shape parameters that can be used to adjust it to the data. No filter is applied to the data set images, and the results show that with a good statistical model, it is not needed to rely on filtering the data to decrease the number of false alarms. Therefore, we can have a better tradeoff between resolution and detection performance.
机译：这封信的目的是比较低频超宽带（UWB）合成孔径雷达（SAR）图像中用于目标检测的两种非相干变化检测算法。所考虑的UWB SAR在20至90 MHz的频率范围内工作。两种方法均根据Neyman-Pearson准则采用似然比检验。首先，将二元瑞利概率分布用于实现似然比检验函数。这种分布是众所周知的，并已用于低频UWB SAR中的变化检测算法，效果良好。为了最大程度地降低误报率，并考虑到低频UWB SAR图像与透射波长相比具有高分辨率，第二种方法通过使用双变量K分布来实现测试。该分布具有比例和形状参数，可用于将其调整为数据。没有对数据集图像应用过滤器，结果表明，有了良好的统计模型，就不需要依靠过滤数据来减少错误警报的数量。因此，我们可以在分辨率和检测性能之间进行更好的权衡。

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