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 indicate 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.

翻译：本文介绍了以波长分辨率合成孔径雷达(SAR)图像进行地面场景预测的五种不同的统计方法。普惠制图像可以用作变化探测算法的参考图像,该算法的发现概率高,而且假警报率低。预测以图像堆积为基础,由同一场景在不同瞬间以同一飞行几何法获得的图像组成。考虑的地面场景预测方法包括:(一) 自转式模型;(二) 倾斜平均值;(三) 中位值;(四) 强度平均值;(五) 平均值。预计预测图像显示真实的地面场景,而没有变化,并保存地面反射模式。研究显示,中位方法提供了真实地面的最准确的描述。为了显示普惠制的适用性,考虑使用中位地面场景作为参考图。因此,中位方法显示在考虑隐藏在森林中的军用车辆时,发现97美元的可能性和0.11km美元虚警报率。