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A New Maximum-Likelihood Change Estimator for Two-Pass SAR Coherent Change Detection

机译:用于两次通过SAR相干变化检测的新的最大似然变化估计器

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摘要

In past research, two-pass repeat-geometry synthetic aperture radar (SAR) coherent change detection (CCD) predominantly utilized the sample degree of coherence as a measure of the temporal change occurring between two complex-valued image collects. Previous coherence-based CCD approaches tend to show temporal change when there is none in areas of the image that have a low clutter-to-noise power ratio. Instead of employing the sample coherence magnitude as a change metric, in this paper, we derive a new maximum-likelihood (ML) temporal change estimate—the complex reflectance change detection (CRCD) metric to be used for SAR coherent temporal change detection. The new CRCD estimator is a surprisingly simple expression, easy to implement, and optimal in the ML sense. This new estimate produces improved results in the coherent pair collects that we have tested.
机译:在过去的研究中,两遍重复几何合成孔径雷达(SAR)相干变化检测(CCD)主要利用采样相干度来衡量两个复数值图像集合之间发生的时间变化。当图像中没有低杂波噪声功率比的区域时,以前的基于相干性的CCD方法往往会显示时间变化。在本文中,我们没有采用样本相干量作为变化量度,而是获得了一个新的最大似然(ML)时间变化估计值-用于SAR相干时间变化检测的复数反射率变化检测(CRCD)量度。新的CRCD估计器是一个出乎意料的简单表达式,易于实现,并且在ML方面是最佳的。在我们测试的相干对集合中,这一新的估计会产生更好的结果。

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