极化干涉SAR植被高度反演算法研究

发布时间：2018-04-28 01:38

本文选题：极化干涉SAR + 散射相位　；参考：《中南大学》2013年硕士论文

【摘要】：极化干涉SAR是一门结合极化SAR和干涉SAR优点的新兴技术,既能获得观测目标的精细物理特征和纹理特征,又能够反映观测目标的空间分布特性。通过极化散射矩阵分解技术在分辨单元内实现多种散射机制相位中心分离,为植被覆盖下的地表地形测量和植被高度估计提供了可能。因此,极化干涉SAR技术可被用于植被参数反演,对地面资源和生态环境的监测有着重要的意义。本文对极化干涉SAR相关理论和极化干涉SAR植被高度反演算法进行了研究,主要工作和创新点如下： (1)深入分析了两种典型极化干涉SAR散射相位中心估计方法。由于极化干涉SAR散射相位中心估计算法是植被高度反演的关键步骤,于是本文深入研究了基于ESPRIT算法的极化干涉SAR散射相位中心估计算法和基于Freeman-极化干涉SAR互协方差矩阵分解的相位中心估计算法,并通过模拟和真实极化干涉SAR数据验证算法的性能。实验结果发现：基于ESPRIT相位估计算法能较好地估计出植被冠层顶部相位中心,但是难以有效估计出地面散射相位中心；而基于Freeman-极化干涉SAR互协方差矩阵分解的相位中心估计算法能够较好地估计出地面散射相位。 (2)结合ESPRIT相位中心估计算法及Freeman-极化干涉互协方差矩阵分解的相位中心估计算法的优点,提出了一种改进的ESPRIT的植被高度反演算法。该算法解决了传统ESPRIT植被高度算法中估计的地面散射相位不准确这一问题。通过选取模拟和真实极化干涉SAR数据进行实验,验证了改进的算法较之传统的方法能够获得更高的反演精度。 (3)提出了一种结合Freeman-极化干涉互协方差矩阵分解及RVOG模型的植被高度反演算法。该算法将Freeman-极化干涉分解估算的地面散射相位作为RVOG模型的初始地表相位,然后利用RVOG模型进行植被高度反演。利用模拟和真实的极化干涉SAR数据进行实验,验证了该算法的有效性,结果表明该方法可以获得更高的植被高度反演精度。图：55幅,表：6个,参考文献：52篇。
[Abstract]:Polarimetric interference (SAR) is a new technology which combines the advantages of polarized SAR and interference SAR. It can not only obtain the fine physical and texture features of the observed target, but also reflect the spatial distribution of the observed target. The polarimetric scattering matrix decomposition technique is used to separate the phase centers of various scattering mechanisms in the resolution unit, which makes it possible to measure the surface topography and estimate the height of vegetation under vegetation cover. Therefore, polarimetric interferometry (SAR) can be used to invert vegetation parameters, which is of great significance to the monitoring of surface resources and ecological environment. In this paper, the correlation theory of polarimetric interferometry (SAR) and the vegetation height inversion algorithm of polarimetric interferometry (SAR) are studied. The main work and innovations are as follows: 1) two typical polarimetric interferometric SAR scattering phase center estimation methods are analyzed. Because the phase center estimation algorithm of polarization interference SAR scattering is a key step in vegetation height inversion. In this paper, the phase center estimation algorithm of polarimetric interference SAR scattering based on ESPRIT algorithm and the phase center estimation algorithm based on Freeman-polarization interference SAR cross-covariance matrix decomposition are studied. The performance of the algorithm is verified by simulation and real polarization interference SAR data. The experimental results show that the ESPRIT phase estimation algorithm can estimate the top phase center of the vegetation canopy, but it is difficult to estimate the surface scattering phase center effectively. The phase center estimation algorithm based on Freeman-polarization interferometric SAR cross-covariance matrix decomposition can estimate the surface scattering phase well. Combining the advantages of the ESPRIT phase center estimation algorithm and the phase center estimation algorithm based on Freeman-polarization interference covariance matrix decomposition, an improved vegetation height inversion algorithm for ESPRIT is proposed. The algorithm solves the problem of inaccurate ground scattering phase estimation in the traditional ESPRIT vegetation height algorithm. By selecting simulated and real polarimetric interferometric SAR data, it is verified that the improved algorithm can obtain higher inversion accuracy than the traditional method. A vegetation height inversion algorithm based on Freeman-polarization interference covariance matrix decomposition and RVOG model is proposed. The ground scattering phase estimated by Freeman-polarization interferometric decomposition is taken as the initial surface phase of RVOG model, and then vegetation height inversion is carried out by using RVOG model. The validity of the proposed algorithm is verified by simulation and real polarimetric interferometric SAR data. The results show that the proposed method can obtain higher accuracy of vegetation height inversion. Figs. 55, tables: 6, references: 52.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：TN957.52;P23

【参考文献】