This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicates that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of 97 % and a false alarm rate of 0 . 11 / km 2 , when considering military vehicles concealed in a forest.

中文翻译：

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基于图像栈的波长分辨SAR地面场景预测。

本文提出了五种不同的统计方法，用于波长分辨率合成孔径雷达（SAR）图像中的地面场景预测（GSP）。GSP图像可以用作变化检测算法中的参考图像，从而产生较高的检测概率和较低的误报率。这些预测基于图像堆栈，这些图像堆栈由来自同一场景的，在不同时刻以相同飞行几何形状获取的图像组成。获得地面场景预测的考虑方法包括：（i）自回归模型；（ii）修剪均值；（iii）中位数；（iv）强度平均值；（v）均值。可以预期，预测的图像将呈现真实的地面场景，而不会发生变化，并保留地面的反向散射图案。研究表明，中值法提供了最准确的真实基础表示。为了显示GSP的适用性，考虑使用地面地面场景作为参考图像的变化检测算法。结果，中值方法显示出97％的检测概率和0的误报率。11 / km 2，当考虑到隐藏在森林中的军车时。