基于时间去相关的三阶段森林高度估计方法
Three-stage Tree Height Inversion Algorithm with Compensation for Temporal Decorrelation
投稿时间:2019-09-12  修订日期:2019-12-14
DOI:
中文关键词:  极化干涉SAR  时间去相关  植被高度反演
英文关键词:polarimetric interferometry SAR  temporal decorrelation  vegetation height inversion.
基金项目:
作者单位E-mail
程甲州 西安科技大学 44239422@qq.com 
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中文摘要:
      [目的]在利用极化SAR数据反演树高时,时间去相关因子是影响反演精度的主要因素;目前,地面随机运动模型(RMoG)是该领域最有效的模型之一,但地面随机运动模型有着反演困难、耗时过长的缺点。为了改善这个问题,本文提出了简化RMoG模型。[方法]首先忽略地面运动,只保留植被冠层运动,重新改写植被体散射公式;然后对多个相干系数直线拟合出地面相位;再次通过PD极化相干最优方法来估计纯体散射去相干值;最后利用改写后的植被体散射公式建立查找表,在固定消光系数的基础上通过查找表反演得到植被高度。为了验证本文方法的有效性,以瑞典南部的 Remingstorp 地区为研究区,以BioSAR2007项目的遥感数据进行试验,并以决定系数(R2)和均方根误差(RMSE)对四种模型的反演结果进行比较评价。[结果]本文方法可以很好地改善三阶段算法的高估问题。在精度比较方面:三阶段算法的R2为0.78,RMSE为8.52;RMoG模型的R2为0.47,RMSE为4.17;RMoGL模型的R2为0.48,RMSE为2.50;本文方法的R2为0.53,RMSE为6.24。对比三阶段算法,本文方法在精度上有明显的优势;对比RMoG模型和RMoGL模型,本文可有效地减少反演时间。[结论]通过添加植被冠层运动消除时间去相关的影响行之有效。与三阶段算法、RMoG模型和RMoGL模型对比,本文方法具有精度高、耗时少的优点,对我国机载或星载极化SAR系统的森林资源监测有重要的参考价值。
英文摘要:
      [Objective] When polarimetric SAR data are used to invert tree height, time decorrelation factor is the main factor affecting inversion accuracy.At present, RMoG is one of the most effective models in this field, but the ground random motion model (RMoG) has the disadvantages of difficult inversion and long time-consuming. In order to improve this problem, a simplified RMoG model is proposed in this paper. [Method] Firstly, the ground motion is neglected and the vegetation canopy motion is retained, and the vegetation volume scattering formula is rewritten; then, the ground phase is judged by linear fitting of multiple coherence coefficients; thirdly, the decoherence value of pure volume scattering is estimated by PD polarization coherence optimization method; finally, the rewritten vegetation volume scattering formula is used to establish a survey. Based on the fixed extinction coefficient, the height of vegetation can be retrieved by looking-up table. To verify the validity of this method, the remote sensing data of BioSAR 2007 project are tested in the Remingstorp area of southern Sweden. The inversion results of the four models are compared and evaluated with the determination coefficient (R2) and the root mean square error (RMSE). [Result] This method can improve the overestimation problem of three-stage algorithm. In terms of accuracy comparison, R2 of three-stage algorithm is 0.78 and RMSE is 8.52; R2 of RMoG model is 0.47 and RMSE is 4.17; R2 of RMoGL model is 0.48 and RMSE is 2.50; R2 of this method is 0.53 and RMSE is 6.24. Compared with the three-stage algorithm, this method has obvious advantages in accuracy; compared with RMoG model and RMoGL model, this paper can effectively reduce the inversion time. [Conclusion] It is effective to eliminate time-related effects by adding vegetation canopy movement. Compared with three-stage algorithm, RMoG model and RMoGL model, this method has the advantages of high accuracy and less time-consuming. It has important reference value for forest resource monitoring of airborne or spaceborne polarimetric SAR system in China.
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