Programa de Pós-Graduação em Estatística, Universidade Federal de Pernambuco, Recife 50670-901, Brazil.
Departamento de Engenharia de Telecomunicações, Universidade Federal do Pampa, Alegrete 97546-550, Brazil.
Sensors (Basel). 2020 Apr 3;20(7):2008. doi: 10.3390/s20072008.
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.
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）图像的地面场景预测（GSP）。GSP 图像可用作变化检测算法中的参考图像，从而实现高检测概率和低虚警率。预测基于图像堆栈，这些堆栈由在相同飞行几何形状下不同时刻获取的同一场景的图像组成。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）中值；（iv）强度均值；和（v）均值。预计预测图像将呈现真实的地面场景而没有变化，并保留地面后向散射模式。研究表明，中值方法提供了对真实地面的最准确表示。为了展示 GSP 的适用性，考虑了一种使用中值地面场景作为参考图像的变化检测算法。结果表明，当考虑隐藏在森林中的军用车辆时，中值方法的检测概率为 97%，虚警率为 0.11/km²。
