利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm. Sometimes the features of target and noise are very alike.
另外，许多分类错误是由SAR图像的像素点类别混淆和相干斑噪声干扰引起的。
Besides, many classification errors are caused by mixed pixels and speckle noise of the SAR image.
利用单个特征识别强噪声中的弱小运动目标，常因所提取的目标特征与噪声特征易混淆而导致高的虚警率。
Recognition algorithms for small moving target in strong noise based on single feature has a high false alarm.
具体分析了以矩形光栅为例的周期性目标莫尔条纹式的混淆效应，以及非周期性目标噪声形式的混淆效应。
Moire effect as an aliasing phenomenon of periodic objects and aliasing contents as signal dependent noise of non-periodic objects are analysed.
具体分析了以矩形光栅为例的周期性目标莫尔条纹式的混淆效应，以及非周期性目标噪声形式的混淆效应。
Moire effect as an aliasing phenomenon of periodic objects and aliasing contents as signal dependent noise of non-periodic objects are analysed.
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