张建双,范文义,毛学刚,于颖.单基线PolInSAR森林高度反演方法研究[J].农业机械学报,2018,49(10):220-229.
ZHANG Jianshuang,FAN Wenyi,MAO Xuegang,YU Ying.Comparison of Five Methods to Inverse Forest Height from Single-baseline PolInSAR Data[J].Transactions of the Chinese Society for Agricultural Machinery,2018,49(10):220-229.
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单基线PolInSAR森林高度反演方法研究   [下载全文]
Comparison of Five Methods to Inverse Forest Height from Single-baseline PolInSAR Data   [Download Pdf][in English]
投稿时间:2018-04-20  
DOI:10.6041/j.issn.1000-1298.2018.10.025
中文关键词:  单基线森林高度反演  DEM差分法  RVoG法  复相干幅度反演法  混合反演法  相干优化法
基金项目:国家重点研发计划项目(2017YFB0502700)和中央高校基本科研业务费专项资金项目(2572018BA02)
作者单位
张建双 东北林业大学 
范文义 东北林业大学 
毛学刚 东北林业大学 
于颖 东北林业大学 
中文摘要:极化干涉SAR(Polarimetric SAR interferometry, PolInSAR)森林高度反演是当前雷达遥感领域的研究热点,近年来出现了多种单基线PolInSAR森林高度反演方法。为了给单基线PolInSAR反演森林高度的算法提供基础,并探索和发展效果更优的反演方法,使用PolSARpro软件模拟森林平均高度为18m的L波段(L=23cm)全极化干涉SAR 数据,研究了森林高度反演算法中的DEM差分法、RVoG法、复相干幅度反演法、混合反演法,并基于相干优化法对混合反演法进行了改进;为了更准确地对算法的性能进行比较,给出方位向为48bin时各算法的距离向剖面的对比图,并选取图像的中间区域,对森林高度位于3~30m的1104个样本点,应用均值和均方根误差RSME对5种方法模拟的18m森林高度进行比较。结果表明:森林高度平均值反演结果由大到小依次为:复相干幅度反演法、混合反演法、改进的混合反演法、RVoG法、DEM差分法,分别为19.40、18.31、18.12、10.55、10.05m,均方根误差(RMSE)由小到大依次为: 改进的混合反演法、混合反演法、复相干幅度反演法、RVoG法、DEM差分法,分别为1.06、1.48、3.49、7.51、8.04m;说明DEM差分法与RVoG法反演的森林高度存在明显低估,复相干反演法出现明显高估且其离散程度最大,混合反演法和改进的混合反演法与真实值的误差分别为0.31、0.12m,改进的混合反演法与真实值的相差最小,离散程度最小,均方根误差最小,反演结果最优。改进的混合反演法综合了混合反演法与相干优化法的优点,使其估计的地形相位的均方根误差最小(0.045rad),森林高度与真实值的误差最小,均方根误差最小,并且具有一定的鲁棒性。
ZHANG Jianshuang  FAN Wenyi  MAO Xuegang  YU Ying
Northeast Forestry University,Northeast Forestry University,Northeast Forestry University and Northeast Forestry University
Key Words:single-baseline forest height inversion  DEM difference  RVoG  SINC  Hybrid  coherent optimization
Abstract:To provide the basis for the methods of vegetation height inversion by using single-baseline PolInSAR SAR data and explore a more effective inversion method, the European Space Agency (ESA) Toolbox PolSARPro was used to simulate L-band (L=23cm) PolInSAR SAR data with an average vegetation height of 18m. The DEM difference, RVoG, SINC, Hybrid, and Hybrid method were studied based on coherent optimization. The vegetation height ranged from 3m to 30m was analyzed with 1104 sample points in the middle region of the image. Giving a 3D image, range profile image with azimuth of 48 and statistical image of vegetation height and topographic phase were used to compare the performance of five methods. Compared with the real value of 18m, the descending order of vegetation height means was SINC, Hybrid, improved Hybrid, RVoG and DEM difference method. The difference between the improved Hybrid inversion method and the real value was the smallest as 0.12m, smaller than Hybrid of 0.31m. RMSE of the improved Hybrid, Hybrid, SINC, RVoG and DEM difference was 1.06m, 1.48m,3.49m, 7.51m and 8.04m, respectively. The vegetation height of the improved Hybrid method had the smallest difference and RMSE. The estimated topographic phase average value of the improved Hybrid, RVoG/Hybrid and DEM difference method was -0.018rad, 0.011rad and 0.1rad;RMSE was 0.045rad, 0.054rad and 0.15rad;and mean value of absolute value was 0.03rad, 0.04rad, and 0.1rad, respectively. The topographic phase of the improved Hybrid method was approximately the closest to the simulated and had the smallest RMSE and the mean of absolute value. Improved Hybrid inversion method produced the best result among the five methods, combining the merits of Hybrid with the coherent optimization, with the smallest difference between real value and RMSE of vegetation height and topographic phase. The Hybrid method was improved based on the coherent optimization and the accuracy of vegetation height was analyzed with the ground phase estimation results to compare the five methods.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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