Recently, it was demonstrated that low-frequency wavelength-resolution synthetic aperture radar (SAR) images could be considered to follow an additive mixing model due to their backscatter characteristics. This simplification allows for the use of source separation methods, such as robust principal component analysis (RPCA) via principal component pursuit (PCP), for detecting changes in those images. In this manuscript, a change detection method for wavelength-resolution SAR images based on image stack through RPCA is proposed. The method aims to explore both the temporal and flight heading diversity of a set of wavelength-resolution multitemporal SAR images in order to detect concealed targets in forestry areas. A heuristic based on three rules for better exploring the RPCA results is introduced, and a new configurable parameter for false alarm reduction based on the analysis of image windows is proposed. The method is evaluated using real data obtained from measurements of the ultrawideband (UWB) very high-frequency (VHF) SAR system CARABAS-II. Experiments for stacks of four and seven reference images are conducted, and the use of reference images acquired with different flight headings is explored. The results indicate that a gain in performance can be achieved by using large image stacks containing, at least, one image of each possible flight heading of the data set, which can result in a probability of detection (PD) above 99% for a false alarm rate (FAR) as low as one false alarm per three square kilometers. Furthermore, it is demonstrated that high PD and low FAR can be achieved, also considering images from similar flight headings as reference images.

中文翻译：

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鲁棒主成分分析的基于图像栈的波长分辨SAR变化检测方法

最近，已证明低频波长分辨率合成孔径雷达（SAR）图像由于其反向散射特性而可以考虑遵循加性混合模型。这种简化允许使用源分离方法，例如通过主成分追踪（PCP）进行鲁棒的主成分分析（RPCA），以检测这些图像中的变化。本文提出了一种基于RPCA的基于图像堆栈的波长分辨率SAR图像变化检测方法。该方法旨在探索一组波长分辨率多时相SAR图像的时空和飞行航向分集，以检测林区中的隐蔽目标。引入了基于三个规则的启发式方法，以更好地探索RPCA结果，提出了一种基于图像窗口分析的减少误报的新可配置参数。使用从超宽带（UWB）甚高频（VHF）SAR系统CARABAS-II的测量获得的真实数据评估该方法。进行了四个和七个参考图像堆栈的实验，并探索了使用具有不同飞行方向的参考图像。结果表明，通过使用大型图像堆栈（至少包含数据集每个可能的飞行航向的一张图像）可以提高性能，这可能导致错误检测的概率（PD）高于99％报警率（FAR）低至每三平方公里1次虚警。此外，已证明可以实现高PD和低FAR，