融合升降轨的极化干涉SAR三层模型植被高度反演方法

doi:10.11947/j.AGCS.2017.20170122

测绘学报 ›› 2017, Vol. 46 ›› Issue (11): 1868-1879.doi: 10.11947/j.AGCS.2017.20170122

融合升降轨的极化干涉SAR三层模型植被高度反演方法

沈鹏¹, 汪长城^1,2, 朱建军¹, 高晗¹, 付海强¹, 解清华¹, 王赛¹, 何帅帅¹

1. 中南大学地球科学与信息物理学院, 湖南长沙 410083;
2. 中南大学有色金属成矿预测教育部重点实验室, 湖南长沙 410083

收稿日期:2017-03-22 修回日期:2017-09-08 出版日期:2017-11-20 发布日期:2017-12-05
通讯作者: 汪长城 E-mail:wangchangcheng@csu.edu.cn
作者简介:沈鹏(1994-),男,硕士生,研究方向为极化干涉SAR数据处理。E-mail:shenpengcsu@163.com
基金资助:
国家自然科学基金（41531068；41371335；41671356）；湖南省自然科学基金（2016JJ2141）；湖南省重点研发计划（2016SK2003）；欧空局数据合作计划（14655）；中南大学研究生自主探索创新项目（2017zzts549）

Vegetation Height Inversion Method with Three-layer Model by Fusing the Ascending and Descending PolInSAR Data

SHEN Peng¹, WANG Changcheng^1,2, ZHU Jianjun¹, GAO Han¹, FU Haiqiang¹, XIE Qinghua¹, WANG Sai¹, HE Shuaishuai¹

1. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
2. Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University), Ministry of Education, Changsha 410083, China

Received:2017-03-22 Revised:2017-09-08 Online:2017-11-20 Published:2017-12-05
Supported by:
The National Natural Science Foundation of China (Nos. 41531068 41371335 41671356) The National Natural Science Foundation of Hunan Province of China (No. 2016JJ2141) The Planned Science and Technology Project of Hunan Province, China (No. 2016SK2003) PA-SB ESA EO Project Campaign (No. 14655) Innovation Foundation for Postgraduate of Central South University (No. 2017zzts549)

摘要/Abstract

摘要：

森林参数的获取不仅可以估算地表生物量和林下地形，还有助于研究全球碳循环和分析全球气候变化。极化干涉SAR植被参数反演算法一般是基于随机地体两层模型（RVoG），但是当实际植被有着冠层、树干层和地表层的明显三层结构时，植被参数反演精度就会变差；另外，由于机载SAR系统数据的近距远距垂直向波数差异较大，导致试验结果存在着由其引起的系统误差。针对这两个问题，本文提出了一种融合升降轨的极化干涉SAR三层模型植被参数反演方法。该方法首先采用三层植被RVoG模型修正微波在穿透植被时的散射过程；然后采用融合升降轨道数据的方式削弱其系统误差；最后，采用非线性迭代平差的反演算法来进行植被高度反演。为了验证该方法的有效性，采用了德国宇航局DLR提供的BioSAR2008项目的两景升轨及两景降轨E-SAR P波段全极化SAR数据进行试验，并采用3组反演策略进行比较分析。结果表明，三层植被模型能够更好地描述植被散射过程；同时，新方法有效降低了由垂直向波数引起的系统误差，提高了树高反演精度。

关键词: RVoG(random volume over ground), 植被高度, 三层植被模型, 三阶段算法, 升降轨, 垂直向波数

Abstract:

The acquisition of forest parameters can not only estimate the surface biomass and underlying topography,but also contribute to the study of global carbon cycle and global climate change.Vegetation parameter inversion algorithm with polarimetric interferometric SAR (PolInSAR) is generally based on the two-layer RVoG(random volume over ground) model.However,when the actual vegetation has three-layer structure of canopy,trunk layer and surface layer,the vegetation parameters inversion accuracy will decrease.As the vertical effective wave number difference between the near and far range in the case of airborne SAR system is large,it will bring the system error to the final inversion results.To solve these two problems,this paper proposes an algorithm of three-layer vegetation parameters inversion by fusing the ascending and descending PolInSAR data.The proposed method uses the three-layer RVoG model to correct scattering process of radar echo in vegetation.Then it combines the ascending and descending PolInSAR datasets to weaken the system errors; Finally,we use the non-linear iteration adjustment for tree height inversion.In order to validate the proposed algorithm,two ascending and two descending P-band full polarization SAR data acquired by ESAR airborne platform under the German space agency (DLR) BioSAR 2008 campaign are utilized and other three inversion strategies are used for comparison and analysis.The results prove the correctness of the three vegetation model,and the proposed method reduces the system error caused by the vertical effective wave number and improves the precision of tree height inversion.

Key words: random volume over ground, vegetation height, three-layer vegetation model, three-stage algorithm, ascending and descending, vertical wave number

中图分类号:

P237

沈鹏, 汪长城, 朱建军, 高晗, 付海强, 解清华, 王赛, 何帅帅. 融合升降轨的极化干涉SAR三层模型植被高度反演方法[J]. 测绘学报, 2017, 46(11): 1868-1879.

SHEN Peng, WANG Changcheng, ZHU Jianjun, GAO Han, FU Haiqiang, XIE Qinghua, WANG Sai, HE Shuaishuai. Vegetation Height Inversion Method with Three-layer Model by Fusing the Ascending and Descending PolInSAR Data[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(11): 1868-1879.

参考文献

[1] LOPEZ-MARTINEZ C,PAPATHANASSIOU K P.Cancellation of Scattering Mechanisms in PolInSAR:Application to Underlying Topography Estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2013,51(2):953-965.
[2] NEUMANN M,FERRO-FAMIL L,REIGBER A.Estimation of Forest Structure,Ground,and Canopy Layer Characteristics from Multibaseline Polarimetric Interferometric SAR Data[J].IEEE Transactions on Geoscience and Remote Sensing 2010,48(3):1086-1104.
[3] CLOUDE S R,PAPATHANASSIOU K P.Polarimetric SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,1998,36(5):1551-1565.
[4] GARESTIER F,DUBOIS-FERNANDEZ P C,PAPATHANASSIOU K P.Pine Forest Height Inversion Using Single-pass X-band PolInSAR Data[J] IEEE Transactions on Geoscience and Remote Sensing,2008,46(1):59-68.
[5] YAMADA H,YAMAGUCHI Y,KIM Y,et al.Polarimetric SAR Interferometry for Forest Analysis Based on the ESPRIT Algorithm (Special Issue on New Technologies in Signal Processing for Electromagnetic-wave Sensing and Imaging)[J].IEICE Transactions on Electronics,2001,E84-C(12):1917-1924.
[6] 谈璐璐,杨立波,杨汝良.基于ESPRIT算法的极化干涉SAR植被高度反演研究[J].测绘学报,2011,40(3):296-300. TAN Lulu,YANG Libo,YANG Ruliang.Investigation of Tree Height Retrieval with Polarimetric SAR Interferometry Based on ESPRIT Algorithm[J].Acta Geodaetica et Cartographica Sinica,2011,40(3):296-300.
[7] PAPATHANASSIOU K P,CLOUDE S R.Single-baseline Polarimetric SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(11):2352-2363.
[8] CLOUDE S R,PAPATHANASSIOU K P.Three-stage Inversion Process for Polarimetric SAR Interferometry[J].IEE Proceedings:Radar,Sonar and Navigation,2003,150(3):125-134.
[9] 谈璐璐,陈兵,杨汝良.利用PolInSAR数据反演植被高度的改进三阶段算法[J].系统仿真学报,2010,22(4):996-999. TAN Lulu,CHEN Bing,YANG Ruliang.Improved Three-stage Algorithm of Tree Height Retrieval with PolInSAR Data[J].Journal of System Simulation,2010,22(4):996-999.
[10] 李新武,郭华东,廖静娟,等.航天飞机极化干涉雷达数据反演地表植被参数[J].遥感学报,2002,6(6):424-429. LI Xinwu,GUO Huadong,LIAO Jingjuan,et al.Inversion of Vegetation Parameters Using Spaceborne Polarimetric SAR Interferometry[J].Journal of Remote Sensing,2002,6(6):424-429.
[11] LOPEZ-SANCHEZ J M,BALLESTER-BERMAN J D,MARQUEZ-MORENO Y.Model Limitations and Parameter-estimation Methods for Agricultural Applications of Polarimetric SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2007,45(11):3481-3493.
[12] 朱建军,解清华,左廷英,等.复数域最小二乘平差及其在PolInSAR植被高反演中的应用[J].测绘学报,2014,43(1):45-51.DOI:10.13485/j.cnki.11-2089.2014.0007. ZHU Jianjun,XIE Qinghua,ZUO Tingying,et al.Criterion of Complex Least Squares Adjustment and Its Application in Tree Height Inversion with PolInSAR Data[J].Acta Geodaetica et Cartographica Sinica,2014,43(1):45-51.DOI:10.13485/j.cnki.11-2089.2014.0007.
[13] 付海强,朱建军,汪长城,等.极化干涉SAR植被高反演复数最小二乘平差法[J].测绘学报,2014,43(10):1061-1067.DOI:10.13485/j.cnki.11-2089.2014.0170. FU Haiqiang,ZHU Jianjun,WANG Changcheng,et al.Polarimetric SAR Interferometry Vegetation Height Inversion Method of Complex Least Squares Adjustment[J].Acta Geodaetica et Cartographica Sinica,2014,43(10):1061-1067.DOI:10.13485/j.cnki.11-2089.2014.0170.
[14] 付海强.基于测量平差理论的PolInSAR植被垂直结构提取模型与方法[D].长沙:中南大学,2014. FU Haiqiang.Theory and Method of Extracting Vegetation Vertical Structure with PolInSAR Based on Surveying Adjustment[D].Changsha:Central South University,2014.
[15] 罗环敏.基于极化干涉SAR的森林结构信息提取模型与方法[D].成都:电子科技大学,2011. LUO Huanmin.Models and Methods of Extracting Forest Structure Information by Polarimetric SAR Interferometry[D].Chengdu:University of Electronic Science and Technology of China,2011.
[16] TREUHAFT R N,MADSEN S N,MOGHADDAM M,et al.Vegetation Characteristics and Underlying Topography from Interferometric Radar[J].Radio Science,1996,31(6):1449-1485.
[17] PAPATHANASSIOU K P,CLOUDE S R.The Effect of Temporal Decorrelation on the Inversion of Forest Parameters from PolInSAR Data[C]//Proceedings of the 2003 IEEE International Geoscience and Remote Sensing Symposium.Toulouse,France:IEEE,2003:1429-1431.
[18] LAVALLE M,SIMARD M,HENSLEY S.A Temporal Decorrelation Model for Polarimetric Radar Interferometers[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(7):2880-2888.
[19] LU Hongxi,SUO Zhiyong,GUO Rui,et al.S-RVoG Model for Forest Parameters Inversion over Underlying Topography[J].Electronics Letters,2013,49(9):618-620.
[20] BAMLER R,HARTL P.Synthetic Aperture Radar Interferometry[J].Inverse Problems,1998,14(4):R1-R54.
[21] SEYMOUR M S,CUMMING I G.Maximum Likelihood Estimation for SAR Interferometry[C]//Surface and Atmospheric Remote Sensing:Technologies,Data Analysis and Interpretation,International Geoscience and Remote Sensing Symposium,1994.Pasadena,CA:IEEE,1994,4:2272-2275.
[22] ROUEFF A,ARNAUBEC A,DUBOIS-FERNANDEZ P C,et al.Cramer-Rao Lower Bound Analysis of Vegetation Height Estimation with Random Volume over Ground Model and Polarimetric SAR Interferometry[J].IEEE Geoscience and Remote Sensing Letters,2011,8(6):1115-1119.
[23] TABB M,ORREY J,FLYNN T,et al.Phase Diversity:A Decomposition for Vegetation Parameter Estimation Using Polarimetrie SAR Interferometry[C]//Proceedings of 4th European Synthetic Aperture Radar Conference.Cologne:EUSAR,2002:721-724.
[24] 解清华,汪长城,朱建军,等.顾及地形因素的S-RVOG模型和PD相干最优算法联合反演植被高度[J].测绘学报,2015,44(6):686-693.DOI:10.11947/j.AGCS.2015.20130731. XIE Qinghua,WANG Changcheng,ZHU Jianjun,et al.Forest Height Inversion by Combining S-RVOG Model with Terrain Factor and PD Coherence Optimization[J].Acta Geodaetica et Cartographica Sinica,2015,44(6):686-693.DOI:10.11947/j.AGCS.2015.20130731.
[25] TEBALDINI S,ROCCA F.Multibaseline Polarimetric SAR Tomography of a Boreal Forest at P-and L-bands[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(1):232-246.
[26] NEUMANN M,SAATCHI S S,ULANDER L M H,et al.Assessing Performance of L-and P-band Polarimetric Interferometric SAR Data in Estimating Boreal Forest Above-ground Biomass[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(3):714-726.
[27] LAVALLE M,KHUN K.Three-baseline InSAR Estimation of Forest Height[J].IEEE Geoscience and Remote Sensing Letters,2014,11(10):1737-1741.
[28] FU Haiqiang,WANG Changcheng,ZHU Jianjun,et al.Inversion of Vegetation Height from PolInSAR Using Complex Least Squares Adjustment Method[J].Science China Earth Sciences,2015,58(6):1018-1031.
[29] FU Haiqiang,WANG Changcheng,ZHU Jianjun,et al.Estimation of Pine Forest Height and Underlying DEM Using Multi-baseline P-band PolInSAR Data[J].Remote Sensing,2016,8(10):820.
[30] WANG Changcheng,WANG Lei,FU Haiqiang,et al.The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data[J].Remote Sensing,2016,8(4):291.
[31] LEE S K,KUGLER F,PAPATHANASSIOU K P,et al.Quantifying Temporal Decorrelation over Boreal Forest at L-and P-band[C]//Proceedings of the 7th European Conference on Synthetic Aperture Radar (EUSAR).Friedrichshafen,Germany:IEEE,2008:1-4.

融合升降轨的极化干涉SAR三层模型植被高度反演方法

Vegetation Height Inversion Method with Three-layer Model by Fusing the Ascending and Descending PolInSAR Data

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	解清华, 汪长城, 朱建军, 付海强. 顾及地形因素的S-RVOG模型和PD相干最优算法联合反演植被高度[J]. 测绘学报, 2015, 44(6): 686-693.
[2]	宋桂萍汪长城付海强解清华. 植被高度的极化干涉互协方差矩阵分解反演法[J]. 测绘学报, 2014, 43(6): 613-636.
[3]	朱建军解清华左廷英汪长城谢建. 复数域最小二乘平差及其在POLInSAR植被高反演中的应用[J]. 测绘学报, 2014, 43(1): 45-51.