SPICE-Based SAR Tomography over Forest Areas Using a Small Number of P-Band Airborne F-SAR Images Characterized by Non-Uniformly Distributed Baselines
SPICE-Based SAR Tomography over Forest Areas Using a Small Number of P-Band Airborne F-SAR Images Characterized by Non-Uniformly Distributed Baselines
复制标题
使用少量基线不均匀分布的 P 波段机载 F-SAR 图像对森林地区进行基于 SPICE 的 SAR 层析成像
DOI：
10.3390/rs11080975
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发表时间：
2019
期刊：
影响因子：
5
通讯作者：
Xie Qinghua
中科院分区：
文献类型：
--
作者：
Peng Xing;Li Xinwu;Wang Changcheng;Zhu Jianjun;Liang Lei;Fu Haiqiang;Du Yanan;Yang Zefa;Xie Qinghua
合成孔径雷达层析成像（TomoSAR）已被证明是一个有用的方法来重建森林地区的垂直结构与P波段图像，由于其三维成像能力。在少量非均匀分布采集的情况下，TomoSAR通常采用压缩感知（CS）。然而，CS的性能取决于所选择的超参数，这与像素的噪声密切相关。在本文中，为了克服这一限制，我们提出了一个稀疏迭代基于协方差估计（SPICE）方法的基础上，小波和正交稀疏基（W&O-SPICE）的森林地区的应用。SPICE是一种稀疏谱估计方法，它实现了高的垂直分辨率，并考虑到每个分辨率单元的噪声自适应。因此，它不需要用户选择超参数。此外，所采用的稀疏基既保证了林冠散射贡献的稀疏性，又能保持地面散射贡献的原始稀疏信息。模拟实验结果表明，该方法能较好地重建森林的垂直结构。此外，三个P-波段全极化机载合成孔径雷达图像与非均匀分布的基线被应用于重建的垂直结构的热带森林在Mabounie，加蓬。估计了下垫面地形和森林高度，相对于LiDAR数字地形模型（DTM）和冠层高度模型（CHM）的均方根误差（RMSE）分别为6.40 m和4.50 m。此外，W&O-SPICE表现出比W&O-CS、波束形成、Capon和迭代自适应方法（IAA）更好的性能。
Synthetic aperture radar tomography (TomoSAR) has been proven to be a useful way to reconstruct vertical structure over forest areas with P-band images, on account of its three-dimensional imaging ability. In the case of a small number of non-uniformly distributed acquisitions, compressive sensing (CS) is generally adopted in TomoSAR. However, the performance of CS depends on the selected hyperparameter, which is closely related to the noise of a pixel. In this paper, to overcome this limitation, we propose a sparse iterative covariance-based estimation (SPICE) approach based on the wavelet and orthogonal sparse basis (W&O-SPICE) for application over forest areas. SPICE is a sparse spectral estimation method that achieves a high vertical resolution, and takes account of the noise adaptively for each resolution cell. Thus, it does not require the user to select a hyperparameter. Furthermore, the used sparse basis not only ensures the sparsity of the forest canopy scattering contribution, but it can also keep the original sparse information of the ground contribution. The proposed method was tested in simulated experiments and the results demonstrated that W&O-SPICE can successfully reconstruct the vertical structure of a forest. Moreover, three P-band fully polarimetric airborne SAR images with non-uniformly distributed baselines were applied to reconstruct the vertical structure of a tropical forest in Mabounie, Gabon. The underlying topography and forest height were estimated, and the root-mean-square errors (RMSEs) were 6.40 m and 4.50 m with respect to the LiDAR digital terrain model (DTM) and canopy height model (CHM), respectively. In addition, W&O-SPICE showed a better performance than W&O-CS, beamforming, Capon, and the iterative adaptive approach (IAA).