论文标题
基于图像堆栈的波长分辨率SAR地面场景预测
Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack
论文作者
论文摘要
本文在波长分辨率合成孔径雷达（SAR）图像中介绍了五种地面场景预测（GSP）的五种不同的统计方法。 GSP图像可以用作更改检测算法中的参考图像，得出很高的检测可能性和较低的错误警报率。这些预测基于图像堆栈，这些图像堆栈由从相同飞行几何形状的不同瞬间获取的同一场景中的图像组成。获得地面场景预测的考虑方法包括（i）自回归模型； （ii）修剪平均值； （iii）中位数； （iv）强度平均值； （v）卑鄙。预计预测的图像会呈现真实的地面场景而不会改变并保留地面反向散射图案。该研究表明，中位方法提供了真正基础的最准确表示。为了显示GSP的适用性，考虑使用中位地面场景作为参考图像，考虑了更改检测算法。结果，当考虑隐藏在森林中的军用车辆时，中位方法显示了$ 97 \％$ $ $ $ $ $ $ $^2的误报率的可能性。
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.
