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首页> 外文期刊>Remote Sensing >Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT)
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Super-Resolved Multiple Scatterers Detection in SAR Tomography Based on Compressive Sensing Generalized Likelihood Ratio Test (CS-GLRT)

机译：基于压缩感知广义似然比检验（CS-GLRT）的SAR层析成像中超分辨多散射体检测

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The application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range resolution cell, and on the reconstruction of their reflectivity profiles. In this work, we build on the existing methods of compressive sensing (CS) and generalized likelihood ratio test (GLRT), and develop a multiple scatterers detection method named CS-GLRT to automatically recognize the number of scatterers superimposed within a single pixel as well as to reconstruct the backscattered reflectivity profiles of the detected scatterers. The proposed CS-GLRT adopts a two-step strategy. In the first step, an L1-norm minimization is carried out to give a robust estimation of the candidate positions pixel by pixel with super-resolution. In the second step, a multiple hypothesis test is implemented in the GLRT to achieve model order selection, where the mapping matrix is constrained within the afore-selected columns, namely, within the candidate positions, and the parameters are estimated by least square (LS) method. Numerical experiments on simulated data were carried out, and the presented results show its capability of separating the closely located scatterers with a quasi-constant false alarm rate (QCFAR), as well as of obtaining an estimation accuracy approaching the Cramer–Rao Low Bound (CRLB). Experiments on real data of Spotlight TerraSAR-X show that CS-GLRT allows detecting single scatterers with high density, distinguishing a considerable number of double scatterers, and even detecting triple scatterers. The estimated results agree well with the ground truth and help interpret the true structure of the complex or buildings studied in the SAR images. It should be noted that this method is especially suitable for urban areas with very dense infrastructure and man-made buildings, and for datasets with tightly-controlled baseline distribution.
机译：随着现代高分辨率星载卫星的发展，SAR层析成像技术（TomoSAR）在城市基础设施和其他人造建筑物上的应用越来越受到欢迎。城市层析成像技术的重点是在一个方位角范围的分辨单元内分离叠加目标，并重建其反射率剖面。在这项工作中，我们以压缩感知（CS）和广义似然比测试（GLRT）的现有方法为基础，并开发了一种名为CS-GLRT的多散射体检测方法，以自动识别单个像素内叠加的散射体数量重建检测到的散射体的反向散射反射率分布。建议的CS-GLRT采用两步策略。第一步，执行L1范数最小化，以超分辨率对每个像素的候选位置进行可靠的估计。第二步，在GLRT中执行多重假设检验以实现模型顺序选择，其中映射矩阵被约束在预先选择的列内，即候选位置内，并且参数以最小二乘（LS ） 方法。对模拟数据进行了数值实验，结果表明，该方法能够以近似恒定的虚警率（QCFAR）分离位置较近的散射体，并能获得接近Cramer-Rao低界（ CRLB）。对Spotlight TerraSAR-X的真实数据进行的实验表明，CS-GLRT可以检测高密度的单个散射体，区分出大量的双重散射体，甚至可以检测三重散射体。估计结果与地面实况非常吻合，并有助于解释在SAR图像中研究的建筑物或建筑物的真实结构。应该注意的是，该方法特别适用于基础设施非常密集的城市和人造建筑物，以及基线分布受到严格控制的数据集。

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