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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height
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A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height

机译：基于最大似然的非参数迭代自适应合成孔径雷达层析成像方法及其在基础地形和林高估计中的应用

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Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar. It is usual to apply nonparametric spectral estimation methods with a large number of snapshots over forest areas. The nonparametric iterative adaptive approach for amplitude and phase estimation (IAA-APES) can obtain a high resolution; however, it only tends to work well with a small number of snapshots. To overcome this problem, this paper proposes the nonparametric iterative adaptive approach based on maximum likelihood estimation (IAA-ML) for the application over forest areas. IAA-ML can be directly used in forest areas, without any prior information or preprocessing. Moreover, it can work well in the case of a large number of snapshots. In addition, it mainly focuses on the backscattered power around the phase centers, helping to detect their locations. The proposed IAA-ML estimator was tested in simulated experiments and the results confirmed that IAA-ML obtains a higher resolution than IAA-APES. Moreover, six P-band fully polarimetric airborne SAR images were applied to acquire the structural parameters of a forest area. It was found that the results of the HH polarization are suitable for analyzing the ground contribution and the results of the HV polarization are beneficial when studying the canopy contribution. Based on this, the underlying topography and forest height of a test site in Paracou, French Guiana, were estimated. With respect to the Light Detection and Ranging (LiDAR) measurements, the standard deviation of the estimations of the IAA-ML TomoSAR method was 2.11 m for the underlying topography and 2.80 m for the forest height. Furthermore, compared to IAA-APES, IAA-ML obtained a higher resolution and a higher estimation accuracy. In addition, the estimation accuracy of IAA-ML was also slightly higher than that of the SKP-beamforming technique in this case study.
机译：合成孔径雷达层析成像（TomoSAR）是获取长波数据集（如L波段和P波段雷达）的基础地形和森林高度的重要方法。通常在森林地区应用具有大量快照的非参数频谱估计方法。用于幅度和相位估计的非参数迭代自适应方法（IAA-APES）可以获得高分辨率。但是，它只能与少量快照配合使用。为了克服这个问题，本文提出了一种基于最大似然估计的非参数迭代自适应方法，用于森林地区的应用。 IAA-ML可以直接在森林地区使用，而无需任何事先信息或预处理。此外，在有大量快照的情况下，它可以很好地工作。此外，它主要关注相位中心周围的反向散射功率，有助于检测其位置。在模拟实验中测试了提出的IAA-ML估计量，结果证实IAA-ML比IAA-APES获得更高的分辨率。此外，应用了六张P波段全极化机载SAR图像来获取林区的结构参数。结果表明，HH极化的结果适合于分析地面贡献，而HV极化的结果对于研究冠层贡献是有益的。基于此，估算了法属圭亚那帕拉库的一个测试点的基础地形和森林高度。关于光探测和测距（LiDAR）测量，IAA-ML TomoSAR方法的估计值的标准偏差为下层地形为2.11 m，森林高度为2.80 m。此外，与IAA-APES相比，IAA-ML获得了更高的分辨率和更高的估计精度。此外，在此案例研究中，IAA-ML的估计精度也略高于SKP波束形成技术。

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