A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data
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基于TSVD的单基线PolInSAR数据反演森林高度方法
DOI：
10.3390/app7050435
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发表时间：
2017-04
期刊：
影响因子：
--
通讯作者：
Zhang B.
中科院分区：
文献类型：
--
作者：
Lin D. F.;Zhu J. J.;Fu H. Q.;Xie Q. H.;Zhang B.
随机地面体积（RVoG）模型将植被垂直结构参数与多个复杂的干涉相干观测量相关联。基于RVoG模型，提出了一种基于截断奇异值分解（TSVD）的单基线极化干涉合成孔径雷达（PolInSAR）数据森林高度反演方法。此外，为了提高TSVD对该问题的适用性，提出了一种新的截断方法。与传统的三阶段反演方法不同，基于TSVD的反演方法直接从复干涉相干信息中估计出纯体积相干信息，并利用最小二乘法从估计出的纯体积相干信息中估计出森林高度。因此，基于TSVD的方法可以调整极化的贡献，在模型参数的估计，并避免了零地面体积比的假设。模拟实验结果表明，基于TSVD的方法在森林高度反演方面优于三阶段方法。TSVD为基础的方法也适用于E-SAR P波段数据采集Krycklan流域，瑞典，这是覆盖着混合松林。结果表明，与三阶段方法相比，基于TSVD的方法将均方根误差提高了48.6%，进一步验证了基于TSVD的方法的性能。
The random volume over ground (RVoG) model associates vegetation vertical structure parameters with multiple complex interferometric coherence observables. In this paper, on the basis of the RVoG model, a truncated singular value decomposition (TSVD)-based method is proposed for forest height inversion from single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. In addition, in order to improve the applicability of TSVD for this issue, a new truncation method is proposed for TSVD. Differing from the traditional three-stage method, the TSVD-based inversion method estimates the pure volume coherence directly from the complex interferometric coherence, and estimates the forest height from the estimated pure volume coherence with a least-squares method. As a result, the TSVD-based method can adjust the contributions of the polarizations in the estimation of the model parameters and avoid the null ground-to-volume ratio assumption. The simulated experiments undertaken in this study confirmed that the TSVD-based method performs better than the three-stage method in forest height inversion. The TSVD-based method was also applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden, which is covered with mixed pine forest. The results showed that the TSVD-based method improves the root-mean-square error by 48.6% when compared to the three-stage method, which further validates the performance of the TSVD-based method.
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影响因子：
1.6
作者：
R. Treuhaft;S. Madsen;M. Moghaddam;J. V. Zyl
通讯作者：
R. Treuhaft;S. Madsen;M. Moghaddam;J. V. Zyl
影响因子：
8.2
作者：
C. López-Martínez;K. Papathanassiou
通讯作者：
C. López-Martínez;K. Papathanassiou
DOI：
10.1109/tgrs.2013.2296533
发表时间：
2014-10-01
影响因子：
8.2
作者：
Kugler, Florian;Schulze, Daniel;Papathanassiou, Konstantinos P.
通讯作者：
Papathanassiou, Konstantinos P.
影响因子：
8.2
作者：
F. Garestier;P. Dubois-Fernandez;K. Papathanassiou
通讯作者：
F. Garestier;P. Dubois-Fernandez;K. Papathanassiou
DOI：
10.1109/tgrs.2009.2037748
发表时间：
2010-05-01
影响因子：
8.2
作者：
Tebaldini, Stefano
通讯作者：
Tebaldini, Stefano