结合相干系数的极化干涉SAR植被高度估计方法研究

Vegetation Height Inversion Algorithms Based on the Coherence of Polarimatric Interferometric SAR

  • 摘要: 阐述了极化干涉SAR成像技术的基本理论,分析了极化干涉SAR的植被散射模型和Cloude三阶段植被高度反演算法。因三阶段法采用多参数初值迭代求解方法,其运算量较大复杂性高,该文提出基于相干系数的高度估计方法。先用干涉相位估计植被高度,再由相干系数幅度估计植被高度对前面相位估计的高度进行补偿,既保证了一定植被高度估计精度,又大大减少了反演算法的运算量,最后用极化干涉SAR仿真数据验证了该方法的有效性。

     

    Abstract: Basic theory of polarimetric synthetic aperture radar (SAR) interferometry is expatiated and the PolInSAR vegetation scattering model and Cloude three-stage vegetation height inversion algorithm are analyzed. The calculation of the three-stage algorithm is complex as it adopts initial value iterative. The paper introduces a method to combine the vegetation height estimation by the coherence coefficient amplitude and phase. This method not only keeps the precision of vegetation height estimation but also reduces the calculation of the algorithm greatly. Finally, the polarimetric SAR interferometry simulated data are used to validated the proposed method.

     

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