1.辽宁工程技术大学测绘与地理科学学院,辽宁 阜新,123000
2.中南大学地球科学与信息物理学院,湖南 长沙,410083
3.阜新生态集团数字科技研究院有限公司,辽宁 阜新,123000
张兵,博士,副教授,研究方向为极化干涉SAR参数反演、复数测量平差理论。zhangbing@lntu.edu.cn
朱建军,博士,教授。zjj@csu.edu.cn
收稿:2024-12-27,
纸质出版:2025-08-05
移动端阅览
张兵,赵薇,朱建军,等.联合邻域像素观测信息的单基线PolInSAR森林高度反演算法[J].武汉大学学报(信息科学版),2025,50(8):1571-1582.
ZHANG Bing,ZHAO Wei,ZHU Jianjun,et al.A Single-Baseline PolInSAR Forest Height Inversion Algorithm with Joint Neighborhood Pixel Observation Information[J].Geomatics and Information Science of Wuhan University,2025,50(8):1571-1582.
张兵,赵薇,朱建军,等.联合邻域像素观测信息的单基线PolInSAR森林高度反演算法[J].武汉大学学报(信息科学版),2025,50(8):1571-1582. DOI:10.13203/j.whugis20240498
ZHANG Bing,ZHAO Wei,ZHU Jianjun,et al.A Single-Baseline PolInSAR Forest Height Inversion Algorithm with Joint Neighborhood Pixel Observation Information[J].Geomatics and Information Science of Wuhan University,2025,50(8):1571-1582. DOI:10.13203/j.whugis20240498
目前极化合成孔径雷达干涉测量(polarimetric interferometric synthetic aperture radar,PolInSAR)技术在森林高度反演领域应用最为广泛的模型为随机地体二层散射模型(random volume over ground model,RVoG),但模型未知参数较多,基于单基线PolInSAR数据难以进行参数解算。鉴于此,提出了联合单基线邻域像素PolInSAR观测信息的森林高度反演算法,基于传统RVoG模型,假设在邻域像素内森林高度及消光系数保持不变,地体幅度比参数随像素改变而改变。该方法可以理解为以牺牲部分空间分辨率换取丰富的观测信息,解决单基线配置下基于RVoG模型的参数反演必须借助先验信息的问题。通过采用覆盖Krycklan针叶林实验区以及Mabounie热带雨林实验区的PolInSAR数据,对所提出的联合邻域像素单基线森林高度反演算法进行了实验验证,结果表明,所提方法相对传统解算策略在针叶林实验区均方根误差分别减小25%及19%,在热带雨林实验区均方根误差分别减小23%及15%。该方法适用于森林高度反演。
Objectives
2
The most widely used model for polarimetric interferometric synthetic aperture radar (PolInSAR) in forest height inversion is random volume over ground (RVoG) model. However
there are many unknown parameters in the model
and it is difficult to solve the parameters based on single baseline PolInSAR data.
Methods
2
In view of this
a forest height inversion method that combines the PolInSAR observation information of single-baseline neighboring pixels is proposed
based on traditional RVoG model
assuming that the forest height and extinction coefficient remain unchanged within the neighboring pixels
and that the ground-body magnitude ratio parameter changes with pixel changes. The method can be understood as sacrificing part of the spatial resolution for rich observation information
which solves the problem that the parameter inversion based on RVoG model in the single-baseline configuration has to rely on a priori information.
Results
2
The proposed joint-neighborhood pixel single-baseline forest height inversion algorithm is experimentally validated using PolInSAR data covering the Krycklan coniferous forest experimental area and the Mabounie rainforest experimental area
and the results show that
compared with traditional solving strategy
the root mean square error of the proposed method are reduced by 25% and 19% for the coniferous forest experimental area
and by 23% and 15% for the rainforest experimental area
respectively.
Conclusions
2
Therefore
the single-baseline PolInSAR forest height inversion algorithm with joint domain pixels can obtain higher accuracy compared to traditional solution strategy and is more suitable for forest height inversion.
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