TerraSAR-X/TanDEM-X升降轨双基干涉模式获取DEM方法研究

秦小芳; 张华春; 张衡; 王宇; 刘华有

doi:10.12000/JR18022

TerraSAR-X/TanDEM-X升降轨双基干涉模式获取DEM方法研究

DOI: 10.12000/JR18022

秦小芳^1,2, ,,
张华春^1,,
张衡^1,2,,
王宇^1,,
刘华有^1,2,

①.
中国科学院电子学研究所北京 100190
②.
中国科学院大学北京 100049

基金项目: 国家重点研发计划（2017YFB0502700）

详细信息

作者简介:
秦小芳(1994–)，女，湖北人，中国科学院电子学研究所信号与信息处理专业硕士研究生，研究方向为星载多基线干涉。E-mail: qinxiaofang15@mails.ucas.ac.cn

张华春(1965–)，男，中国科学院电子学研究所研究员，研究方向为合成孔径雷达系统集成测试与信号处理。E-mail: hczhang@mail.ie.ac.cn

张衡：张衡(1990–)，男，山东人，中国科学院电子学研究所通信与信息系统专业博士研究生，研究方向为双基信号处理技术。E-mail: caszhmail@163.com

王宇：王宇(1980–)，男，河南人，现为中国科学院电子学研究所研究员，博士生导师，研究方向为SAR系统设计与信号处理技术。E-mail: yuwang@mail.ie.ac.cn

刘华有(1994–)，男，江西人，中国科学院电子学研究所通信与信息系统专业硕士研究生，研究方向为多通道InSAR高程重建方法。E-mail: liuhuayou15@mails.ucas.ac.cn

通讯作者:
秦小芳 qinxiaofang2017@163.com

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出版历程
- 收稿日期: 2018-03-15
- 修回日期: 2018-06-12
- 网络出版日期: 2018-08-28

A High Precision DEM Generation Method Based on Ascending and Descending Pass TerraSAR-X/TanDEM-X BiSAR Data

Qin Xiaofang^{1,2
, ,},
Zhang Huachun^1
,,
Zhang Heng^{1,2
,},
Wang Yu^1
,,
Liu Huayou^{1,2
,}

①.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
②.
University of Chinese Academy of Sciences, Beijing 100049, China

Funds: National Key RD Program of China (2017YFB0502700)

摘要

摘要: 该文基于TerraSAR-X/TanDEM-X (TSX/TDX)双基升降轨数据，首先采用非局部干涉(NonLocal Interferometric SAR, NL-InSAR)相位滤波分别得到单航过升轨和降轨模式下的高分辨率DEM。在此基础上，基于NL-InSAR估计得到的较准确相干系数，提出一种升降轨DEM融合方法，恢复SAR侧视成像造成的几何畸变，提高DEM重建精度。该文采用两幅北京地区的TSX/TDX升降轨干涉对进行融合处理，结果表明，在地形复杂地区的叠掩和阴影等无效区域，融合之后的DEM无效点数明显减少。经统计，融合后无效点数比例由升轨、降轨的4.93%和4.52%降低到1.34%。同时，融合DEM的精度相比于升轨的6.74 m提高了8.7%、相比于降轨的6.67 m提高了9.6%，融合后高程精度达到6.09 m。
- 数字高程模型 /
- 非局部干涉参数估计 /
- 升降轨融合 /
- 高分辨率 /
- 高精度
Abstract: A method for fine resolution and high precision Digital Elevation Model (DEM) generation using ascending and descending pass TerraSAR-X/TanDEM-X (TSX/TDX) datasets is proposed in this study. First, the NonLocal Interferometric SAR (NL-InSAR) can effectively generate ascending and descending pass raw DEMs. On this basis, the coherence well recovered by NL-InSAR is used to fusion the raw DEMs to further improve the accuracy and reduce the invalids caused by layover and shadows. This method was used to process the TSX/TDX data obtained in Beijing. The number of invalid points of DEM after fusion decreased significantly. Statistics result shows that it decreased from 4.93% in ascending raw DEM and 4.52% in descending raw DEM to 1.34% in the fusion DEM. At the same time, the accuracy of the fusion DEM increased by 9.6% compared to 6.67 m in the descending raw DEM and 8.7% compared to 6.74 m in the ascending raw DEM, reaching 6.09 m.
- Digital Elevation Model (DEM) /
- NonLocal Interferometric SAR (NL-InSAR) /
- Fusion of ascending and descending /
- High resolution /
- High precision

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图 1 非局部滤波

Figure 1. Non-local filtering

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图 2 相干性估计

Figure 2. Coherence estimates

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图 3 SAR影像几何畸变图

Figure 3. Geometric distortion of SAR image

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图 4 升降轨飞行示意图

Figure 4. Sketch map of ascending and descending

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图 5 升降轨DEM预处理

Figure 5. Preprocessing of ascending and descending raw DEMs

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图 6 升降轨处理流程图

Figure 6. Flow chart of ascending and descending raw DEMs

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图 7 双基模式及实验研究区域

Figure 7. the bistatic mode and the research area

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图 8 城区滤波效果对比图

Figure 8. Contrast of different filters

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图 9 相干系数图

Figure 9. Coherence by different filters

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图 10 DEM对比图

Figure 10. DEM contrast by different methods

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图 11 升降轨单基线DEM(红色方框部分是公共区域)

Figure 11. Ascending and descending pass raw DEM (Overlaid red boundary is the common arear between two datasets)

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图 12 升降轨融合后的DEM(部分城区)

Figure 12. Fusion DEM of ascending and descending pass (City)

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图 13 升降轨融合后的DEM(部分山区)

Figure 13. Fusion DEM of ascending and descending pass (Hill)

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表 1 升降轨DEM融合处理表

Table 1. Logic table for ascending and descending pass TanDEM-X raw DEMs fusion

升轨叠掩 /阴影	降轨叠掩 /阴影	升轨相干系数小于阈值	降轨相干系数小于阈值	融合DEM
1	1	–	–	无效值
1	0	–	1	无效值
1	0	–	0	降轨h
0	1	1	–	无效值
0	1	0	–	升轨h
0	0	0	1	升轨h
0	0	1	0	降轨h
0	0	0	0	加权
0	0	1	1	无效值

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表 2 数据集

Table 2. Datasets

数据类型	时间	轨道	垂直基线(m)	入射角(°)	模糊高度(m)
TDX/TSX	2014-08-19	升轨	104.27	43.34	73.31
TDX/TSX	2014-04-07	降轨	89.22	45.86	93.21

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表 3 缺失区域统计表

Table 3. Statistics table of invalids

处理方法	无效点比例(%)
升轨	4.93
降轨	4.52
融合	1.34

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表 4 与SRTM DEM对比高程残差统计表(SRTM)

Table 4. Comparison of height difference with respect to SRTM DEM

处理方法	山区	平地
升轨DEM (m)	13.55	7.37
降轨DEM (m)	12.80	6.81
融合后DEM (m)	11.56	6.61

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表 5 高程精度

Table 5. Accuracy of different DEMs

序号	纬度(°)	经度(°)	控制点高程值(m)	升轨DEM (m)		降轨DEM (m)		融合DEM (m)
序号	纬度(°)	经度(°)	控制点高程值(m)	高程值	误差	高程值	误差	高程值	误差
1	39.787079N	116.386766E	36.46	46.19	9.73	47.61	11.15	46.42	9.96
2	39.946029N	116.180640E	80.67	78.64	–2.03	75.55	–5.12	77.28	–3.39
3	39.936176N	116.163946E	83.03	76.40	–6.63	81.13	–1.90	80.00	–3.03
4	39.933121N	116.158557E	90.82	96.75	5.93	95.70	4.88	96.15	5.33
均方根误差				6.74		6.67		6.09

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参考文献(23)

[1]	蒋厚军. 高分辨率星载InSAR技术在DEM生成及更新中的应用研究[D]. [博士论文], 武汉大学, 2012: 1–2 Jiang Hou-jun. High-resolution spaceborne SAR interferometry for DEM generation and updating[D]. [Ph.D. dissertation], Wuhan University, 2012: 1–2
[2]	Farr T G and Kobrick M. Shuttle radar topography mission produces a wealth of data[J]. Eos,Transactions American Geophysical Union, 2000, 81(48): 583–585. DOI: 10.1029/EO081i048p00583
[3]	Krieger G, Moreira A, Fiedler H, et al. TanDEM-X: A satellite formation for high-resolution SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(11): 3317–3341. DOI: 10.1109/TGRS.2007.900693
[4]	邓云凯, 王宇. 先进双基SAR技术研究(英文)[J]. 雷达学报, 2014, 3(1): 1–9. DOI: 10.3724/SP.J.1300.2014.14026 Deng Yunkai and Wang Robert. Exploration of advanced bistatic SAR experiments[J]. Journal of Radars, 2014, 3(1): 1–9. DOI: 10.3724/SP.J.1300.2014.14026
[5]	Rizzoli P, Bräutigam B, Kraus T, et al. Relative height error analysis of TanDEM-X elevation data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73: 30–38. DOI: 10.1016/j.isprsjprs.2012.06.004
[6]	Rizzoli P, Martone M, Gonzalez C, et al. Generation and performance assessment of the global TanDEM-X digital elevation model[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 132: 119–139. DOI: 10.1016/j.isprsjprs.2017.08.008
[7]	Martone M, Rizzoli P, Wecklich C, et al. The global forest/non-forest map from TanDEM-X interferometric SAR data[J]. Remote Sensing of Environment, 2017, 205: 352–373.
[8]	Zhu X X, Lachaise M, Adam F, et al.. Beyond the 12 m TanDEM-X DEM[C]. Proceedings of 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 2014: 390–393
[9]	斯奇, 王宇, 邓云凯, 等. 一种基于最大后验框架的聚类分析多基线干涉SAR高度重建算法[J]. 雷达学报, 2017, 6(6): 640–652. DOI: 10.12000/JR17043 Si Qi, Wang Yu, Deng Yunkai, et al. A novel cluster-analysis algorithm based on MAP framework for multi-baseline InSAR height reconstruction[J]. Journal of Radars, 2017, 6(6): 640–652. DOI: 10.12000/JR17043
[10]	Gao X M, Liu Y L, Li T, et al. High precision DEM generation algorithm based on InSAR multi-look iteration[J]. Remote Sensing, 2017, 9(7): 741. DOI: 10.3390/rs9070741
[11]	杜亚男, 冯光财, 李志伟, 等. TerraSAR-X/TanDEM-X获取高精度数字高程模型技术研究[J]. 地球物理学报, 2015, 58(9): 3089–3102 Du Yanan, Feng Guangcai, Li Zhiwei, et al. Generation of high precision DEM from TerraSAR-X/TanDEM-X[J]. Chinese Journal of Geophysics, 2015, 58(9): 3089–3102
[12]	Deledalle C A, Denis L, and Tupin F. NL-InSAR: Nonlocal interferogram estimation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(4): 1441–1452. DOI: 10.1109/TGRS.2010.2076376
[13]	靳国旺, 徐青, 张红敏. 合成孔径雷达干涉测量[M]. 北京: 国防工业出版社, 2014: 169–183 Jin Guo-wang, Xu Qing, and Zhang Hong-min. Synthetic Aperture Radar Interferometry[M]. Beijing: National Defend Industry Press, 2014: 169–183
[14]	刘辉, 靳国旺, 张红敏, 等. DEM辅助的山区InSAR相位解缠[J]. 测绘科学技术学报, 2017, 34(2): 215–220. DOI: 10.3969/j.issn.1673-6338.2017.02.019 Liu Hui, Jin Guo-wang, Zhang Hong-min, et al. Phase unwrapping assisted by DEM of InSAR for mountainous terrain[J]. Journal of Geomatics Science and Technology, 2017, 34(2): 215–220. DOI: 10.3969/j.issn.1673-6338.2017.02.019
[15]	Kropatsch W G and Strobl D. The generation of SAR layover and shadow maps from digital elevation models[J]. IEEE Transactions on Geoscience and Remote Sensing, 1990, 28(1): 98–107. DOI: 10.1109/36.45752
[16]	Carrasco D, Díaz J, and Broquetas A. Ascending-descending orbit combination SAR interferometry assessment[C]. Proceedings of the 3rd ERS Symposium, Florence, Italy, 1997: 1789–1794
[17]	赵争. 地形复杂区域InSAR高精度DEM提取方法[D]. [博士论文], 武汉大学, 2014 Zhao Zheng. Methods on high-accuracy DEM extraction from interferometric SAR in sophisticated terrain areas[D]. [Ph.D. dissertation], Wuhan University, 2014
[18]	Zhang X J, Zeng Q M, Jiao J, et al. Fusion of space-borne multi-baseline and multi-frequency interferometric results based on extended Kalman filter to generate high quality DEMs[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 111: 32–44. DOI: 10.1016/j.isprsjprs.2015.11.005
[19]	Deo R, Rossi C, Eineder M, et al. Framework for fusion of ascending and descending pass TanDEM-X raw DEMs[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3347–3355. DOI: 10.1109/JSTARS.2015.2431433
[20]	Goodman N R. Statistical analysis based on a certain multivariate complex Gaussian distribution (an introduction)[J]. Annals of Mathematical Statistics, 1963, 34(1): 152–177. DOI: 10.1214/aoms/1177704250
[21]	Deledalle C A, Denis L, Poggi G, et al. Exploiting patch similarity for SAR image processing: The nonlocal paradigm[J]. IEEE Signal Processing Magazine, 2014, 31(4): 69–78. DOI: 10.1109/MSP.2014.2311305
[22]	Seymour M S and Cumming I G. Maximum likelihood estimation for SAR interferometry[C]. Proceedings of 1994 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 1994, 4: 2272–2275
[23]	肖金群. 采用D-InSAR技术获取山区DEM的研究[D]. [硕士论文], 中南大学, 2011: 9–10 Xiao Jin-qun. DEM generation in mountainous area with differential InSAR technique[D]. [Master dissertation], Central South University, 2011: 9–10