一种新的桥梁区域时序InSAR相位解缠方法
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摘要
时间序列InSAR技术是一种空间遥感技术,可用于获取基础设施的高精度微小形变信息,相比传统测量手段,该技术有较多优点,在桥梁形变检测方面有广阔的应用前景。但现有的时间序列InSAR技术中的相位解缠算法在大型斜拉桥形变检测上存在较大解缠误差,导致目前该技术还难以获取正确的斜拉桥形变结果。提出一种新的时间序列InSAR相位解缠算法。通过构建约束三角网络,对残差点进行分块,利用二分图最优权匹配方法获取最优的正负残差点匹配结果,从而获取正确的解缠相位。在洞庭湖大桥数据上进行实验验证。该算法在解缠精度上明显优于稀疏网络最小费用流(MCF)算法,有效减少了解缠误差。
Time series InSAR technique can be used to obtain the deformation of buildings, and its application in the bridge deformation monitoring is important to bridge health monitoring due to many profits of space remote sensing. However, the present phase unwrapping methods of time series InSAR make many errors in deformation monitoring of the cable-stayed bridge. Thus it is difficult to acquire the reasonable deformation results of the bridge. This work proposes a new phase unwrapping algorithm based on bipartite graph matching with partitioned residues. After generating the constrained Delaunay triangulation network, the found residues are then partitioned. Finally, the bipartite graph matching algorithm is used to get the optimal connection of positive and negative residues, and the unwrapped phase is acquired by integration of wrapped phase gradient field. The results, tested on real SAR images of the Dongtinghu Bridge, confirm the effectiveness and reliability of the algorithm.
Time series InSAR technique can be used to obtain the deformation of buildings, and its application in the bridge deformation monitoring is important to bridge health monitoring due to many profits of space remote sensing. However, the present phase unwrapping methods of time series InSAR make many errors in deformation monitoring of the cable-stayed bridge. Thus it is difficult to acquire the reasonable deformation results of the bridge. This work proposes a new phase unwrapping algorithm based on bipartite graph matching with partitioned residues. After generating the constrained Delaunay triangulation network, the found residues are then partitioned. Finally, the bipartite graph matching algorithm is used to get the optimal connection of positive and negative residues, and the unwrapped phase is acquired by integration of wrapped phase gradient field. The results, tested on real SAR images of the Dongtinghu Bridge, confirm the effectiveness and reliability of the algorithm.
引文
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[11] Yu H, Li Z, Bao Z. A fast phase unwrapping method for large-scale interferograms [J]. IEEE Transaction on Geoscience and Remote Sensing, 2013, 51(7): 4 240-4 248.
[12] Martinez-Espla J J, Martinez-Marin T, Lopez-Sanchez J M. A particle filter approach for InSAR phase filtering and unwrapping [J]. IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(4): 1 197-1 211.
[13] Huang Q, Zhou H, Dong S, et al. Parallel branch-cut algorithm based on simulated annealing for large-scale phase unwrapping [J]. IEEE Transaction on Geoscience and Remote Sensing, 2015, 53(7): 3 833-3 846.
[14] Loffeld O, Nies H, Knedlik S, et al. Phase unwrapping for SAR interferometry: a data fusion approach by Kalman filtering [J]. IEEE Transaction on Geoscience and Remote Sensing, 2008, 46(1): 47-58.
[15] Yu H, Li Z, Bao Z. Residues cluster-based segmentation and outlier-detection method for large-scale phase unwrapping [J]. IEEE Transaction on Image Processing, 2011, 20(10): 2 865-2 875.
[16] Liu G, Wang R, Deng Y, et al. A new quality map for 2-D phase unwrapping based on gray level co-occurrence matrix [J]. IEEE Geoscience and Remote Sensing Letter, 2014, 11(2): 444-448.
[17] 杨磊,赵拥军,王志刚.最小生成树相位解缠中冗余去除算法[J].遥感学报,2006,10(6):879-884.
[18] 张妍,冯大政,曲小宁,等.基于改进粒子群算法的二维相位解缠算法[J].电波科学学报,2012(27):1 166-1 123.
[2] 刘国祥,陈强. 永久散射体雷达干涉理论与方法[M]. 北京:科学出版社,2012.
[3] Costantini M. A novel phase unwrapping method based on network programing [J]. IEEE Transaction on Geoscience and Remote Sensing, 1998, 36(3): 813-821.
[4] Ghiglia D, Pritt M. Two-dimensional phase unwrapping: theory, algorithms, and software [M]. New York: Wiley, 1998.
[5] Chen C. Statistical-cost network-flow unwrapping for radar interferometry [D]. Stanford: Stanford University,2001.
[6] Chen C W, Zebker H A. Network approaches to two-dimensional phase unwrapping: intractability and two new algorithms [J]. Journal of the Optical Society of America A, Optics, image science, 2000, 17(3): 401-414.
[7] 于勇,王超,张红,等.基于不规则网络下网络流算法的相位解缠方法[J]. 遥感学报,2003,7(6):472-477.
[8] 魏志强,金亚秋. 基于蚁群算法的InSAR相位解缠算法[J]. 电子与信息学报,2008,30(3):518-523.
[9] Hooper A, Zebker H. Phase unwrapping in three dimensions with applications to InSAR time series [J]. Journal of the Optical Society of America A, Optics, image science, 2007, 24(9): 2 737-3 747.
[10] Costantini M, Malvarosa F, Minati F. A general formulation forredundant integration of finite differences and phase unwrapping on a sparse multidimensional domain [J]. IEEE Transaction on Geoscience and Remote Sensing, 2012, 50(3): 758-768.
[11] Yu H, Li Z, Bao Z. A fast phase unwrapping method for large-scale interferograms [J]. IEEE Transaction on Geoscience and Remote Sensing, 2013, 51(7): 4 240-4 248.
[12] Martinez-Espla J J, Martinez-Marin T, Lopez-Sanchez J M. A particle filter approach for InSAR phase filtering and unwrapping [J]. IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(4): 1 197-1 211.
[13] Huang Q, Zhou H, Dong S, et al. Parallel branch-cut algorithm based on simulated annealing for large-scale phase unwrapping [J]. IEEE Transaction on Geoscience and Remote Sensing, 2015, 53(7): 3 833-3 846.
[14] Loffeld O, Nies H, Knedlik S, et al. Phase unwrapping for SAR interferometry: a data fusion approach by Kalman filtering [J]. IEEE Transaction on Geoscience and Remote Sensing, 2008, 46(1): 47-58.
[15] Yu H, Li Z, Bao Z. Residues cluster-based segmentation and outlier-detection method for large-scale phase unwrapping [J]. IEEE Transaction on Image Processing, 2011, 20(10): 2 865-2 875.
[16] Liu G, Wang R, Deng Y, et al. A new quality map for 2-D phase unwrapping based on gray level co-occurrence matrix [J]. IEEE Geoscience and Remote Sensing Letter, 2014, 11(2): 444-448.
[17] 杨磊,赵拥军,王志刚.最小生成树相位解缠中冗余去除算法[J].遥感学报,2006,10(6):879-884.
[18] 张妍,冯大政,曲小宁,等.基于改进粒子群算法的二维相位解缠算法[J].电波科学学报,2012(27):1 166-1 123.
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