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Robust vegetation height Extraction using maximum likelihood estimation for Dual-baseline PolInSAR

机译：双基线POLINER的最大似然估计鲁棒植被高度提取

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摘要

Polarimetric SAR interferometry technique has been widely used for parameters extraction of the earth's surface vegetation. In this paper, based on the two layers Random Volume over Ground model, we present a vegetation height inversion algorithm for dual-baseline PolInSAR data. The method obtained the ground and volume scattering component respectively by using the theory of Freeman polarimetric decomposition. Then the maximum likelihood estimation of the covariance matrix was used to construct the vegetation height for dual-baseline PolInSAR. The proposed algorithm overcomes the restriction of traditional maximum likelihood estimation method which required the parameters of ground scattering to be known. Finally, the experimental results of L-band PolInSAR simulated data show that the algorithm improves the effect of height estimation compare to the coherence method.
机译：Polarimetric SAR干涉测量技术已广泛用于地球表面植被的参数提取。本文基于两层随机体积在地面模型上，我们为双基线POLINSAR数据提供了一种植被高度反转算法。该方法通过使用Freeman Polariemetric分解的理论分别获得地面和体积散射部件。然后使用协方差矩阵的最大似然估计来构建双基线PONINAR的植被高度。该算法克服了传统最大似然估计方法的限制，该方法需要已知地面散射的参数。最后，L波段PORINSAR模拟数据的实验结果表明，该算法改善了与相干方法相比的高度估计的效果。

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