顾及地表散射贡献与多基线参数线性相关性的PolInSAR植被高反演方法

doi:10.11947/j.AGCS.2023.20200581

测绘学报 ›› 2023, Vol. 52 ›› Issue (1): 51-60.doi: 10.11947/j.AGCS.2023.20200581

顾及地表散射贡献与多基线参数线性相关性的PolInSAR植被高反演方法

林东方^1,2, 朱建军³, 李志伟³, 付海强³, 梁继¹, 周访滨⁴, 张兵⁵

1. 湖南科技大学测绘遥感信息工程湖南省重点实验室, 湖南湘潭 411201;
2. 武汉大学测绘学院, 湖北武汉 430079;
3. 中南大学地球科学与信息物理学院, 湖南长沙 410083;
4. 长沙理工大学湖南省公路先进建养技术国际科技创新合作基地, 湖南长沙 410114;
5. 辽宁工程技术大学测绘与地理科学学院, 辽宁阜新 123000

收稿日期:2021-12-31 修回日期:2022-11-12 发布日期:2023-02-09
通讯作者: 朱建军 E-mail:zjj@csu.edu.cn
作者简介:林东方(1986—),男,博士,讲师,研究方向为测量数据处理与PolInSAR技术应用。E-mail: lindongfang223@163.com
基金资助:
国家自然科学基金(42104025); 湖南省自然科学基金创新研究群体(2020JJ1003); 中国博士后科学基金(2021M702509); 湖南省自然资源科技计划(2022-07);地球空间环境与大地测量教育部重点实验室测绘基础研究基金(20-01-04); 湖南省自然科学基金(2021JJ30244; 2022JJ30254);长沙理工大学湖南省公路先进建养技术国际科技创新合作基地开放基金资助项目(kfj190805)

A multi-baseline PolInSAR forest height inversion method taking into account the ground scattering effects and parametric linear

LIN Dongfang^1,2, ZHU Jianjun³, LI Zhiwei³, FU Haiqiang³, LIANG Ji¹, ZHOU Fangbin⁴, ZHANG Bing⁵

1. Hunan Provincial Key Laboratory of Geo-Information Engineering in Surveying, Mapping and Remote Sensing, Hunan University of Science and Technology, Xiangtan 411201, China;
2. School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China;
3. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China;
4. Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway, Changsha University of Science & Technology, Changsha 410114, China;
5. School of Geomatics, Liaoning Technical University, Fuxin 123000, China

Received:2021-12-31 Revised:2022-11-12 Published:2023-02-09
Supported by:
The National Natural Science Foundation of China (No.42104025);Foundation for Innovative Research Groups of the Natural Science Foundation of Hunan Province (No.2020JJ1003);China Postdoctoral Science Foundation (No.2021M702509);The Natural Resources Sciences and Technology Project of Hunan Province (No.2022-07);Surveying and Mapping Basic Research Foundation of Key Laboratory of Geospace Environment and Geodesy, Ministry of Education (No.20-01-04);The Natural Science Foundation of Hunan Province (Nos. 2021JJ30244;2022JJ30254);Open Fund of Hunan International Scientific and Technological Innovation Cooperation Base of Advanced Construction and Maintenance Technology of Highway (Changsha University of Science & Technology) (No.kfj190805)

摘要/Abstract

摘要： 单基线PolInSAR受观测信息不足影响常假设体散射占优极化方式地体幅度比为零,以实现植被高参数的反演,该假设导致植被高参数反演存在较大偏差。利用多基线观测数据,可补充观测信息实现地体幅度比参数的反演。然而,多基线反演模型引入过多模型参数,造成部分参数存在近似线性相关性,引起反演模型病态,常规算法难以得到准确的模型参数估值。鉴于此,本文提出了植被高反演多基线分步解算方法。首先,基于多基线体散射占优极化数据构建多基线反演函数模型,利用正则化方法处理模型病态问题,获得模型参数正则化估值,并通过均方误差分析方法筛选模型参数估值,确定出可靠的地体幅度比参数估值;然后,利用地体幅度比参数估值重估多基线纯体相干性,并基于最小二范估计准则融合多基线纯体相干性反演植被高参数。试验结果表明,本文方法可有效提高植被高反演精度,相较于三阶段算法与多基线常规解法,反演精度提高了26%,具备较好的可行性和有效性。

关键词: 多基线, 植被高, 地体幅度比, PolInSAR, 病态问题

Abstract: Affected by the insufficient information of single baseline observation data, the three-stage method assume the ground-to-volume ratio (GVR) to be zero so as to invert the vegetation height. However, this assumption introduces much biases into the parameter estimates which greatly limit the accuracy of the vegetation height inversion. Multi-baseline observation can provide redundant information and is helpful for the inversion of GVR. Nevertheless, there are many geometry parameters that share similar values in a multi-baseline model, which lead to ill-posed problems and reduce the inversion accuracy of conventional algorithm. To this end, we propose a new step-by-step inversion method applied to the multi-baseline observations. Firstly, an adjustment inversion model is constructed by using multi-baseline volume scattering dominant polarization data, and the regularized estimates of model parameters are obtained by regularization method. Then, the reliable estimates of GVR is determined by the mean square error (MSE) analysis of each regularized parameter estimation. Secondly, using the estimated GVR extracts the pure volume coherence, and then inverting the vegetation heights from the pure volume coherences by least squares estimation. The experimental results show that the new method can improve the vegetation height inversion result effectively. The inversion accuracy is improved by 26% with respect to three-stage method and the conventional solution of multi-baseline. The results have demonstrated the feasibility and effectiveness of the new method.

Key words: multi-baseline, vegetation height, GVR, PolInSAR, ill-posed problem

中图分类号:

P207
P227

林东方, 朱建军, 李志伟, 付海强, 梁继, 周访滨, 张兵. 顾及地表散射贡献与多基线参数线性相关性的PolInSAR植被高反演方法[J]. 测绘学报, 2023, 52(1): 51-60.

LIN Dongfang, ZHU Jianjun, LI Zhiwei, FU Haiqiang, LIANG Ji, ZHOU Fangbin, ZHANG Bing. A multi-baseline PolInSAR forest height inversion method taking into account the ground scattering effects and parametric linear[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(1): 51-60.

参考文献

[1] SIMARD M, FATOYINBO L, SMETANKA C, et al. Mangrove canopy height globally related to precipitation, temperature and cyclone frequency[J]. Nature Geoscience, 2019, 12(1): 40-45.
[2] MOREIRA A, KRIEGER G, HAJNSEK I, et al. Tandem-L: a highly innovative bistatic SAR mission for global observation of dynamic processes on the earth's surface[J]. IEEE Geoscience and Remote Sensing Magazine, 2015, 3(2): 8-23.
[3] PAN Y, BIRDSEY R A, FANG J, et al. A large and persistent carbon sink in the world's forests[J]. Science, 2011, 333(6045): 988-993.
[4] LE TOAN T, QUEGAN S, DAVIDSON M W J, et al. The BIOMASS mission: mapping global forest biomass to better understand the terrestrial carbon cycle[J]. Remote Sensing of Environment, 2011, 115(11): 2850-2860.
[5] CLOUDE S R, PAPATHANASSIOU K P. Polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5): 1551-1565.
[6] 郭华东, 李新武, 王长林, 等. 极化干涉雷达遥感机制及作用[J]. 遥感学报, 2002, 6(6): 401-405. GUO Huadong, LI Xinwu, WANG Changlin, et al. The mechanism and role of polarimetric SAR interferometry[J]. Journal of Remote Sensing, 2002, 6(6): 401-405.
[7] 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.
[8] TREUHAFT R N, SIQUEIRA P R. Vertical structure of vegetated land surfaces from interferometric and polarimetric radar[J]. Radio Science, 2000, 35(1): 141-177.
[9] PAPATHANASSIOU K P, CLOUDE S R. Single-baseline polarimetric SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(11): 2352-2363.
[10] 解清华, 朱建军, 汪长城, 等. 基于S-RVoG模型的PolInSAR森林高度非线性复数最小二乘反演算法[J]. 测绘学报, 2020, 49(10): 1303-1310.DOI: 10.11947/j.AGCS.2020.20190081. XIE Qinghua, ZHU Jianjun, WANG Changcheng, et al. A S-RVoG model-based PolInSAR nonlinear complex least squares method for forest height inversion[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(10): 1303-1310.DOI: 10.11947/j.AGCS.2020.20190081.
[11] 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.
[12] 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.
[13] PRAKS J, KUGLER F, PAPATHANASSIOU K P, et al. Height estimation of boreal forest: interferometric model-based inversion at L- and X-band versus HUTSCAT profiling scatterometer[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(3): 466-470.
[14] GARESTIER F, DUBOIS-FERNANDEZ P C, CHAMPION I. Forest height inversion using high-resolution P-band Pol-InSAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(11): 3544-3559.
[15] KUGLER F, LEE S K, HAJNSEK I, et al. Forest height estimation by means of Pol-InSAR data inversion: the role of the vertical wavenumber[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5294-5311.
[16] FU Wenxue, GUO Huadong, LI Xinwu, et al. Extended three-stage polarimetric SAR interferometry algorithm by dual-polarization data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(5): 2792-2802.
[17] 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.
[18] MANAGHEBI T, MAGHSOUDI Y, VALADAN ZOEJ M J. An improved three-stage inversion algorithm in forest height estimation using single-baseline polarimetric SAR interferometry data[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(6): 887-891.
[19] MANAGHEBI T, MAGHSOUDI Y, VALADAN ZOEJ M J. A volume optimization method to improve the three-stage inversion algorithm for forest height estimation using PolInSAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(8): 1214-1218.
[20] LEE J S, AINSWORTH T L. The effect of orientation angle compensation on coherency matrix and polarimetric target decompositions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 49(1): 53-64.
[21] XIE Yanzhou, FU Haiqiang, ZHU Jianjun, et al. A LiDAR-aided multibaseline PolInSAR method for forest height estimation: with emphasis on dual-baseline selection[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(10): 1807-1811.
[22] ZHU Jianjun, XIE Qinghua, ZUO Tingying, et al. Complex least squares adjustment to improve tree height inversion problem in PolInSAR[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(1): 1-8.
[23] 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. 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.
[24] FU Haiqiang, ZHU Jianjun, WANG Changcheng, et al. Underlying topography extraction over forest areas from multi-baseline PolInSAR data[J]. Journal of Geodesy, 2018, 92(7): 727-741.
[25] LIN Dongfang, ZHU Jianjun, FU Haiqiang, et al. A TSVD-based method for forest height inversion from single-baseline PolInSAR data[J]. Applied Sciences, 2017, 7(5): 435.
[26] 徐天河, 杨元喜. 均方误差意义下正则化解优于最小二乘解的条件[J]. 武汉大学学报(信息科学版), 2004,29(3): 223-226. XU Tianhe, YANG Yuanxi. Condition of regularization solution superior to LS solution based on MSE principle[J]. Geomatics and Information Science of Wuhan University, 2004, 29(3): 223-226.
[27] SONG Yingchun, XIA Yuguo, XIE Xuemei. Adjustment model and algorithm based on ellipsoid uncertainty[J]. Journal of Geodesy and Geoinformation Science, 2020, 3(3): 59-66.
[28] SHEN Yunzhong, XU Peiliang, LI Bofeng. Bias-corrected regularized solution to inverse ill-posed models[J]. Journal of Geodesy, 2012, 86(8): 597-608.
[29] 林东方, 朱建军, 付海强, 等. 均方误差意义下的正则化参数二次优化方法[J]. 测绘学报, 2020, 49(4): 443-451.DOI: 10.11947/j.AGCS.2020.20190148. LIN Dongfang, ZHU Jianjun, FU Haiqiang, et al. Optimization of regularization parameter based on minimum MSE[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(4): 443-451.DOI: 10.11947/j.AGCS.2020.20190148.
[30] LIN Dongfang, ZHU Jianjun, LI Chaokui, et al. Bias reduction method for parameter inversion of ill-posed surveying model[J]. Journal of Surveying Engineering, 2020, 146(3): 04020011.
[31] KUGLER F, LEE S K, HAJNSEK I, et al. Forest height estimation by means of pol-InSAR data inversion: the role of the vertical wavenumber[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(10): 5294-5311.
[32] LEE S K, FATOYINBO T E, LAGOMASINO D, et al. Multibaseline TanDEM-X mangrove height estimation: the selection of the vertical wavenumber[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3434-3442.

顾及地表散射贡献与多基线参数线性相关性的PolInSAR植被高反演方法

A multi-baseline PolInSAR forest height inversion method taking into account the ground scattering effects and parametric linear

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