[1]余祥伟,薛东剑*,陈凤娇.植被及坡度对SAR干涉相干性的影响分析[J].山地学报,2020,(6):926-934.[doi:10.16089/j.cnki.1008-2786.000568]
　YU Xiangwei,XUE Dongjian*,CHEN Fengjiao.Analysis of Influence of Vegetation Coverage and Slope on SAR Interferometric Coherence[J].Mountain Research,2020,(6):926-934.[doi:10.16089/j.cnki.1008-2786.000568]
点击复制

植被及坡度对SAR干涉相干性的影响分析
分享到：

《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2020年第6期
页码:
926-934
栏目:
山地技术
出版日期:
2020-12-25

文章信息/Info

Title:
Analysis of Influence of Vegetation Coverage and Slope on SAR Interferometric Coherence
文章编号:
1008-2786-(2020)6-926-09
作者:
余祥伟1; 2; 薛东剑1*; 陈凤娇1
1.成都理工大学 地球科学学院,成都 610059; 2.四川省冶金地质勘查院,成都 610051
Author(s):
YU Xiangwei1; 2; XUE Dongjian1*;  CHEN Fengjiao1
1. College of Earth Sciences, Chengdu University of Technology, Chengdu 610059,China; 2. Sichuan Institute of Metallurgical Geology and Exploration, Chengdu 610051, China
关键词:
InSAR;  空间失相关;  地表起伏;  植被覆盖区;  数值模拟;  相关分析
Keywords:
InSAR;  spatial incoherence;  irregular topography;  vegetation-covered area;  numerical simulation;  correlation analysis
分类号:
TP70
DOI:
10.16089/j.cnki.1008-2786.000568
文献标志码:
A
摘要:
相干性是雷达干涉测量的基础,直接影响数据处理的难度和地形提取的精度,其研究对完善InSAR相干性分解模型及改进形变解算方法具有重要意义。本文从分析地表目标对雷达波束的后向散射特征出发,以灰色关联分析及建模分析相结合,重点研究了植被及坡度两个关键去相干源对相干性的影响。以C波段的哨兵1A TOPS模式数据验证了两个因素对干涉测量的限制,在统计分析的基础上,利用灰色关联度探讨了植被、坡度对相干性损失的贡献比例及影响规律,利用采样点建立了植被、坡度与相干性的函数关系,模拟出了三者的经验关系模型并进行了验证。研究发现:(1)植被是高覆被山区SAR影像空间失相干的主导因素,相干性和植被覆盖度之间呈幂函数关系,相关系数为0.5812;(2)相干性随坡度增加平稳衰减,且两者为反比例函数关系,相关系数为0.8027;(3)模型预估两个特征差异区域的相干性与短时间基线影像间的实际相干性接近,平均绝对值误差均低于0.14。本文研究对理解干涉测量条件,完善InSAR相干性估计方法以及指导InSAR数据选取及参数配置具有一定的参考价值。
Abstract:
Coherence is the basis of radar interferometry, which directly increases the difficulties in data processing and affects the accuracy of terrain extraction. Its research is of great significance for perfecting the InSAR coherence decomposition model and improving the deformation monitoring method. Starting from the analysis of the back-scattering characteristics of the radar beam from surface targets, this paper combined gray correlation analysis and modeling analysis, and focused on the impact of vegetation and slope-two key decoherent sources on the coherence. The limitations of the two factors on traditional interferometry were verified by the C-band sentinel 1A TOPS model SAR data. Based on statistical analysis, the gray correlation was used to explore the contribution ratio and influence law of vegetation and slope to the loss of coherence. With sampling points, the functional relationship between fractional vegetation cover, slope and coherence was established, and the empirical relationship model of them was simulated and verified. The study achieved the following:(1)Vegetation was the dominant factor of the spatial incoherence of SAR images space in densely vegetated mountainous areas. There was a power function relation between coherence and fractional vegetation cover, with a correlation coefficient of 0.5812;(2)The coherence decayed steadily as the slope increases. And the relationship between the two was an inverse proportional function, and the correlation coefficient was 0.8027;(3)The model predicted that the coherence of the two feature difference regions was close to the actual coherence of the short-term baseline SAR image series, and the average absolute value error was less than 0.14. The research in this paper has certain reference value and application significance for understanding the conditions of interferometry, establishing improved InSAR coherence estimation model and assisting of researchers to determine the most appropriate data and set the most suitable processing parameters by different deformation characteristics.

参考文献/References:

[1] 王超,张红,刘智. 星载合成孔径雷达干涉测量[M].北京:科学出版社,2002.[WANG Chao, ZHANG Hong, LIU Zhi. Spaceborne synthetic aperture radar interferometry [M].Beijing: Science Press,2002:167-198]
[2] 张诗茄,蒋建军,缪亚敏,等.基于SBAS技术的岷江流域潜在滑坡识别[J].山地学报,2018,36(1):91-97.[ZHANG Shijia, JIANG Jianjun, MIAO Yamin, et al. Application of the SBAS technique in potential landslide identification in the Minjiang Watershed[J]. Mountain Research, 2018, 36(1): 91-97]
[3] 朱建军,李志伟,胡俊. InSAR变形监测方法与研究进展[J].测绘学报,2017,46(10):1717-1733.[ZHU Jianjun, LI Zhiwei, HU Jun. Research progress and methods of InSAR for deformation monitoring[J].Acta Geodaetica et Cartographica Sinica,2017,46(10): 1717-1733]
[4] NANNINI M, MARTONE M, RIZZOLI P, et al. Coherence-based SAR tomography for spaceborne applications[J]. Remote Sensing of Environment, 2019,225:107-114.
[5] GOLDSTEIN R M, WERNER C L. Radar interferogram filtering for geophysical applications[J]. Geophysical Research Letters, 1998,25(21):4035-4038.
[6] AHMED R, SIQUEIRA P, HENSLEY S, et al. A survey of temporal decorrelation from spaceborne L-Band repeat-pass InSAR[J]. Remote Sensing of Environment, 2011, 115(11):2887-2896.
[7] JUNGKYO J,DUK-JIN K,LAVALLE M, et al. Coherent change detection using InSAR temporal decorrelation model: a case study for volcanic ash detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(10):5765-5775.
[8] WANG Teng, LIAO MingSheng, PERISSIN D. InSAR coherence-decomposition analysis[J]. IEEE Geoscience and Remote Sensing Letters, 2010, 7(1):156-160.
[9] SANTOS, RúBEN, CALDEIRA B, BEZZEGHOUD M, et al. The rupture process and location of the 2003 Zemmouri-Boumerdes earthquake(Mw 6.8)inferred from seismic and geodetic data[J].Pure and Applied Geophysics,2015,172(9):2421-2434.
[10] 龙江平,丁晓利. 利用矩阵值域的极化干涉SAR相干性的研究[J]. 武汉大学学报(信息科学版),2015,40(11):1526-1532.[LONG Jiangping, DING Xiaoli. Polarimetric interferometry SAR coherence using the field of matri[J]. Geomatics and Information Science of Wuhan University, 2015,40(11):1526-1532]
[11] OLESK A, PRAKS J, ANTROPOV O, et al. Interferometric SAR coherence models for characterization of hemiboreal forests using TanDEM-X data [J]. Remote Sensing, 2016, 8(9):700-722.
[12] HANSSEN R F. Radar interferometry data interpretation and error analysis[M]. Netherlands: Kluwer Academic Publishers,2001.
[13] ASKNE J, SANTORO M. Multitemporal repeat-pass SAR interferometry of boreal forests[J]. IEEE Transactions on Geoscience and Remote Sensing,2005, 43(6): 1540-1550.
[14] 廖明生,张路,史绪国,等. 滑坡变形雷达遥感监测方法与实践[M].北京:科学出版社,2017:34-35. [LIAO Mingsheng, ZHANG Lu, SHI Xuguo, et al. Method and practice of landslide deformation monitoring by radar remote sensing [M].Beijing:Science Press,2017:34-35〗]
[15] LIU Guang, FAN JingHui, ZHAO Feng, et al. Monitoring elevation change of glaciers on Geladandong Mountain using TanDEM-X SAR interferometry[J].Journal of Mountain Science,2017,14(5):859-869.
[16] 龙江平,刘峰,段祝庚. 联合干涉相位和相干性幅度的极化干涉SAR最优相干性估计[J].测绘学报,2017,46(1):73-82.[LONG Jiangping, LIU Feng, DUAN Zhugeng. A new method of coherence optimization based on the phase and coherence magnitude in polarimetric SAR interferometry[J]. Acta Geodaetica et Cartographica Sinica,2017,46(1):73-82]
[17] 张永红,张继贤,林宗坚,等. 地形引起的雷达辐射畸变及其校正[J].测绘科学,2002,27(4):23-26.[ZHANG Yonghong, ZHANG Jixian, LIN Zongjian,et al. Terrain induced SAR radiometric distortions and their correction[J]. Science of Surveying and Mapping,2002,27(4):23-26]
[18] 田馨,廖明生. InSAR技术在监测形变中的干涉条件分析[J].地球物理学报,2013,56(3):812-823.[TIAN Xin, LIAO Mingsheng. The analysis of conditions for InSAR in the field of deformation monitoring[J]. Chinese Journal of Geophysics,2013,56(3):812-823]
[19] PICHIERRI M, HAJNSEK I, ZWIEBAC S, et al. On the potential of Polarimetric SAR interferometry to characterize the biomass, moisture and structure of agricultural crops at L-, C- and X-Bands[J].Remote Sensing of Environment,2018,204:596-616.
[20] 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.
[21] GUO Bing, HAN Fang, JIANG Lin. An improved dimidiated pixel model for vegetation fraction in the Yarlung Zangbo River Basin of Qinghai-Tibet Plateau [J].Journal of the Indian Society of Remote Sensing,2018,46(2):219-231.
[22] YAZDANI M, KAHRAMAN C, ZARATE P, et al. A fuzzy multi attribute decision framework with integration of QFD and grey relational analysis[J].Expert Systems with Applications,2019,115:474-485.
[23] 李春江,沈国状,张继超. 基于灰色系统理论的植被物理参数与极化分解参数的关联分析——以鄱阳湖湿地为例[J].遥感技术与应用,2019,34(4):839-846.[LI Chunjiang, SHEN Guozhuang, ZHANG Jichao. Correlation analysis of vegetation physical parameters and polari-zation decomposition parameters based on grey system theory: a case study in Poyang Lake Wetland [J]. Remote Sensing Technology and Application,2019, 34(4):839-846]

相似文献/References:

[1]巨淑君,高志良,蒋明成,等.震后大渡河流域丹巴—乐山段地质灾害隐患[J].山地学报,2023,(3):411.[doi:10.16089/j.cnki.1008-2786.000758 ]
　JU Shujun,GAO Zhiliang,et al.Post-Earthquake Hidden Dangers of Geological Hazards in the Danba-Leshan Section of the Dadu River Basin, China[J].Mountain Research,2023,(6):411.[doi:10.16089/j.cnki.1008-2786.000758 ]

备注/Memo

备注/Memo:
收稿日期(Received date):2019-05-31; 改回日期(Accepted date): 2020-12-10
基金项目(Foundation item):四川省科技计划项目(2019YJ0505); 国家重点研发计划子课题(2018YFC0706003-1)。[The Science and Technology Plan Project of Sichuan(2019YJ0505); Sub-project of National Key R&D Program of China(2018YFC0706003-1)]
作者简介(Biography):余祥伟(1994-),男,四川巴中人,硕士研究生,主要研究方向:雷达干涉测量。[YU Xiangwei(1994- ), male,born in Bazhong, Sichuan province, M.Sc., candidate, research on synthetic aperture radar interferometry] E-mail: 601119707@qq.com
*通讯作者(Corresponding author):薛东剑(1977- ),男,博士,副教授,主要研究方向:雷达图像处理及干涉测量。[XUE Dongjian(1977- ), male, Ph.D., associate professor, specialized in radar image processing and interferometry] E-mail: xdj101@sina.com
更新日期/Last Update: 2020-11-30