空间网络对时序InSAR相位解缠的影响——以Delaunay与Dijkstra网络为例

doi:10.11947/j.AGCS.2022.20200469

测绘学报 ›› 2022, Vol. 51 ›› Issue (2): 248-257.doi: 10.11947/j.AGCS.2022.20200469

空间网络对时序InSAR相位解缠的影响——以Delaunay与Dijkstra网络为例

马张烽¹, 蒋弥², 李桂华¹, 黄腾¹

1. 河海大学地球科学与工程学院, 江苏南京 211100;
2. 中山大学测绘科学与技术学院, 广东珠海 519082

收稿日期:2020-09-21 修回日期:2021-03-23 发布日期:2022-02-28
通讯作者: 蒋弥 E-mail:jiangmi@mail.sysu.edu.cn
作者简介:马张烽(1995-),男,博士生,研究方向为InSAR地壳形变监测及地球物理学建模。E-mail:jspcmazhangfeng@hhu.edu.cn
基金资助:
国家自然科学基金（42074008；41774003）；中央高校基本科研业务费专项资金（B210203079）；江苏省研究生科研与实践创新计划项目（KYCX21_0528）

Effects of spatial network on time series InSAR phase unwrapping: take the Delaunay and Dijkstra networks for example

MA Zhangfeng¹, JIANG Mi², LI Guihua¹, HUANG Teng¹

1. School of Earth Sciences and Engineering, Hohai University, Nanjing 211100, China;
2. School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China

Received:2020-09-21 Revised:2021-03-23 Published:2022-02-28
Supported by:
The National Natural Science Foundation of China (Nos. 42074008;41774003);The Fundamental Research Funds for the Central Universities (No. B210203079);Postgraduate Research & Practice Innovation Program of Jiangsu Province (No. KYCX21_0528)

摘要/Abstract

摘要： 在时间序列InSAR相位解缠的过程中，相干目标需事先构建空间网络之后再进行模糊度解算。Delaunay三角网是当前时序相位解缠的主流构网方法，但其网络形态易包含高相位梯度的边缘，导致违背相位连续性假设。考虑到目前很少有关于空间网络对解缠影响的研究及相位解缠对InSAR技术测量精度的主导地位，本文在量化分析Delaunay网络对解缠影响的基础上，提出引入图论中的Dijkstra最短路径算法优化Delaunay网络中所有边的相位梯度，进而改善时序相位解缠的精度。本文采用模拟和真实数据对基于Delaunay网络和基于优化网络的相位解缠进行了对比验证。结果表明，本文提出的构网方法能够更好地满足相位连续性假设，减少约33%由解缠误差所导致的不闭合三角环数。较传统研究聚焦解缠方法和目标函数的改进而言，本文研究揭示了空间网络的改善对时间序列相位解缠的重要性。

关键词: 时间序列, 相位解缠, Dijkstra, 空间网络, InSAR, 相位连续性假设

Abstract: In time series InSAR phase unwrapping, coherent pixels are required to be fully connected by a spatial network and then ambiguity can be estimated. During the network generation, Delaunay triangulation has been an invariable choice for InSAR community, but its network configuration easily contains the edge of high phase gradient, which leads to the violation of phase continuity assumption. Given that whether the geometry of Delaunay is suitable for phase unwrapping or not is rarely discussed, in this paper we quantitatively discussed the performance of Delaunay triangulation in phase unwrapping and we also proposed a new network through introducing the graph theory to improve the unwrapping accuracy. Experimental results validate that the proposed method can better satisfy the phase continuity assumption, and is superior to the Delaunay network for a reduction of ~33% unclosed interferogram triangle loops. Compared to the previous researches focused on unwrapping methods and the improvement of objective function, this study reveals the importance of the improvement of spatial network to the time series phase unwrapping.

Key words: time series, phase unwrapping, Dijkstra, spatial network, InSAR, phase continuity assumption

中图分类号:

P237

马张烽, 蒋弥, 李桂华, 黄腾. 空间网络对时序InSAR相位解缠的影响——以Delaunay与Dijkstra网络为例[J]. 测绘学报, 2022, 51(2): 248-257.

MA Zhangfeng, JIANG Mi, LI Guihua, HUANG Teng. Effects of spatial network on time series InSAR phase unwrapping: take the Delaunay and Dijkstra networks for example[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 248-257.

参考文献

[1] BAMLER R, HARTL P. Synthetic aperture radar interferometry[J]. Inverse Problems, 1998, 14(4):1-54.
[2] 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. DOI:10.11947/j.AGCS.2019.20180188. DOI:10.11947/j.AGCS.2017.20170350. 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. DOI:10.11947/j.AGCS.2019.20180188.DOI:10.11947/j.AGCS.2017.20170350.
[3] TANG Xinming, LI Tao, GAO Xiaoming, et al. Research on key technologies of precise InSAR surveying and mapping applications using automatic SAR imaging[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2):27-37.
[4] ITOH K. Analysis of the phase unwrapping algorithm[J]. Applied Optics, 1982, 21(14):2470.
[5] GHIGLIA D C, ROMERO L A. Minimum Lp-norm two-dimensional phase unwrapping[J]. Josa A, 1996, 13(10):1999-2013.
[6] YU Hanwen, LAN Yang, YUAN Zhihui, et al. Phase unwrapping in InSAR:a review[J]. IEEE Geoscience and Remote Sensing Magazine, 2019, 7(1):40-58.
[7] PEPE A, LANARI R. On the extension of the minimum cost flow algorithm for phase unwrapping of multitemporal differential SAR interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9):2374-2383.
[8] HOOPER A, ZEBKER H A. Phase unwrapping in three dimensions with application to InSAR time series[J]. Josa A, 2007, 24(9):2737-2747.
[9] LI Rui, LÜ X, YUAN Jili, et al. A triangle-oriented spatial-temporal phase unwrapping algorithm based on irrotational constraints for time-series InSAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(12):10263-10275.
[10] DIRICHLET G L. Über Die Reduction der positiven quadratischen Formen mit drei unbestimmten ganzen Zahlen[J]. Journal Für Die Reine Und Angewandte Mathematik (Crelles Journal), 1850, 1850(40):209-227.
[11] WEGMÜLLER U, WERNER C, STROZZI T, et al. Sentinel-1 support in the GAMMA Software[C]//Proceedings of Fringe 2015:Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop. European Space Agency, 2015:Fringe.
[12] LI Rui, LÜ X, YUN Ye. A network-optimization-based L-norm sparse 2D phase unwrapping method for persistent scatterer interferometry[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5):709-713.
[13] 段伟, 吕孝雷. 一种新的桥梁区域时序InSAR相位解缠方法[J]. 中国科学院大学学报, 2019, 36(2):251-258. DUAN Wei, LÜ Xiaolei. A new phase unwrapping algorithm of time series InSAR for large bridges[J]. Journal of University of Chinese Academy of Sciences, 2019, 36(2):251-258.
[14] DIAL R B. Algorithm 360:shortest-path forest with topological ordering[J]. Communications of the ACM, 1969, 12(11):632-633.
[15] COSTANTINI M. A novel phase unwrapping method based on network programming[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3):813-821.
[16] COSTANTINI M, MALVAROSA F, MINATI F. A general formulation for redundant integration of finite differences and phase unwrapping on a sparse multidimensional domain[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(3):758-768.
[17] BIGGS J, WRIGHT T, LU Zhong, et al. Multi-interferogram method for measuring interseismic deformation:Denali Fault, Alaska[J]. Geophysical Journal International, 2007, 170(3):1165-1179.
[18] CARBALLO G F, FIEGUTH P W. Probabilistic cost functions for network flow phase unwrapping[J]. IEEE Transactions on Geoscience and Remote Sensing, 2000, 38(5):2192-2201.
[19] COSTANTINI M, ROSEN P A. A generalized phase unwrapping approach for sparse data[C]//Proceedings of 1999 IEEE International Geoscience and Remote Sensing Symposium. Hamburg, Germany:IEEE, 1999:267-269.
[20] JOHNSON D B. A note on Dijkstra's shortest path algorithm[J]. Journal of the ACM, 1973, 20(3):385-388.
[21] JIANG Mi, GUARNIERI A M. Distributed scatterer interferometry with the refinement of spatiotemporal coherence[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(6):3977-3987.
[22] HANSSEN R F. Radar interferometry[M]. Dordrecht:Springer Netherlands, 2001.
[23] Wright T J, Gonzalez P J, Walters R J, et al. LiCSAR:Tools for automated generation of Sentinel-1 frame interferograms[C]//Proceedings of 2016 AGU Fall Meeting.[S.l.]:AGU Fall Meeting Abstracts, 2016.
[24] DE ZAN F, ZONNO M, LÓPEZ-DEKKER P. Phase inconsistencies and multiple scattering in SAR interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(12):6608-6616.
[25] Fattahi H. Geodetic imaging of tectonic deformation with InSAR[D]. Miami,Florida,USA:University of Miami, 2015.
[26] XU Xiaohua, SANDWELL D T. Toward absolute phase change recovery with InSAR:correcting for earth tides and phase unwrapping ambiguities[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1):726-733.

空间网络对时序InSAR相位解缠的影响——以Delaunay与Dijkstra网络为例

Effects of spatial network on time series InSAR phase unwrapping: take the Delaunay and Dijkstra networks for example

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