This article derives a new change detector for multivariate synthetic aperture radar (SAR) image time series (ITS). Classical statistical change detection methodologies based on covariance matrix analysis are usually built upon the Gaussian assumption, as well as an unstructured signal model. Both of these hypotheses may be inaccurate for high-dimension/resolution images, where the noise can be heterogeneous (non-Gaussian) and where the relevant signals usually lie in a low-dimensional subspace (low-rank structure). These two issues are tackled by proposing a new generalized likelihood ratio test based on a robust (compound Gaussian) low-rank (structured covariance matrix) model. The interest of the proposed detector is assessed on two SAR-ITS set from UAVSAR.

中文翻译：

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多变量 SAR 图像时间序列的鲁棒低秩变化检测

本文推导了一种用于多元合成孔径雷达（SAR）图像时间序列（ITS）的新变化检测器。基于协方差矩阵分析的经典统计变化检测方法通常建立在高斯假设以及非结构化信号模型的基础上。对于高维/分辨率图像，这两种假设都可能不准确，其中噪声可能是异质的（非高斯），并且相关信号通常位于低维子空间（低秩结构）中。通过提出基于稳健（复合高斯）低秩（结构化协方差矩阵）模型的新广义似然比检验来解决这两个问题。所提出的探测器的兴趣是在来自 UAVSAR 的两个 SAR-ITS 集上进行评估的。