| Citation: |
XU Fu, LI Zhenhong, SONG Chuang, CHEN Bo, DU Jiantao, HAN Bingquan, LONG Sichun, PENG Jianbing. An Enhanced Nonlocal Coherent Pixel Selection Method for Time Series Inversion of GBSAR[J]. Geomatics and Information Science of Wuhan University, 2023, 48(11): 1884-1896. DOI: 10.13203/j.whugis20230348
|
Ground-based synthetic aperture radar (SAR) time series analysis mainly selects coherent pixels with strong scattering characteristics and stable phase from SAR images for subsequent time series inversion. Coherent pixels between different interferograms are variable, which makes time series analysis complicated. Therefore, it is crucial to select a high number of coherent pixels that are of high-quality to ensure precision in ground-based SAR deformation monitoring.
To improve the reliability of time series analysis in ground-based SAR, this paper proposed the enhanced nonlocal coherent pixels selection (E-NonPS) method to select effective coherent pixels from the interferogram network, including full-rank coherent pixels and partially coherent pixels. Full-rank coherent pixels are selected by the nonlocal full-rank method, and partially coherent pixels are selected by setting the signal-to-noise ratio (SNR) threshold for pixels with coherence occurrences of more than N/2 in single look complex images.
We used E-NonPS method to test two ground-based SAR datasets to verify the effectiveness in different monitoring environments (e.g., high and low coherence areas), and compared the selection results of coherent pixels with three traditional methods, including amplitude dispersion index (ADI) method, coherence coefficient (Coh) method and nonlocal full-rank coherent pixel selection (NonPS) method. The experimental results show that E-NonPS method can significantly increase the number of selected coherent pixels, and effectively improve the accuracy, especially for low coherence areas.
The propoesed E-NonPS method can effectively address the demands for selecting coherent pixels and ensure sufficient coherent pixels in time series analysis.
| [1] |
吴星辉, 马海涛, 张杰. 地基合成孔径雷达的发展现状及应用[J]. 武汉大学学报(信息科学版), 2019, 44(7): 1073-1081. doi: 10.13203/j.whugis20190058
Wu Xinghui, Ma Haitao, Zhang Jie. Development Status and Application of Ground-Based Synthetic Aperture Radar[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1073-1081. doi: 10.13203/j.whugis20190058
|
| [2] |
Noferini L, Mecatti D, Macaluso G, et al. Monitoring of Belvedere Glacier Using a Wide Angle GB-SAR Interferometer[J]. Journal of Applied Geophysics, 2009, 68(2): 289-293. doi: 10.1016/j.jappgeo.2009.02.004
|
| [3] |
Dematteis N, Luzi G, Giordan D, et al. Monitoring Alpine Glacier Surface Deformations with GB-SAR[J]. Remote Sensing Letters, 2017, 8(10): 947-956. doi: 10.1080/2150704X.2017.1335905
|
| [4] |
Giordan D, Allasia P, Dematteis N, et al. A Low-Cost Optical Remote Sensing Application for Glacier Deformation Monitoring in an Alpine Environment[J]. Sensors, 2016, 16(10): 1750. doi: 10.3390/s16101750
|
| [5] |
刘国祥, 张波, 张瑞, 等. 联合卫星SAR和地基SAR的海螺沟冰川动态变化及次生滑坡灾害监测[J]. 武汉大学学报(信息科学版), 2019, 44(7): 980-995. doi: 10.13203/j.whugis20190077
Liu Guoxiang, Zhang Bo, Zhang Rui, et al. Monitoring Dynamics of Hailuogou Glacier and the Secondary Landslide Disasters Based on Combination of Satellite SAR and Ground-Based SAR[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 980-995. doi: 10.13203/j.whugis20190077
|
| [6] |
Di Traglia F, De Luca C, Manzo M, et al. Joint Exploitation of Space-borne and Ground-based Multitemporal InSAR Measurements for Volcano Monitoring: The Stromboli Volcano Case Study[J]. Remote Sensing of Environment, 2021, 260: 112441. doi: 10.1016/j.rse.2021.112441
|
| [7] |
Intrieri E, Di Traglia F, Del Ventisette C, et al. Flank Instability of Stromboli Volcano (Aeolian Islands, Southern Italy): Integration of GB-InSAR and Geomorphological Observations[J]. Geomorphology, 2013, 201: 60-69. doi: 10.1016/j.geomorph.2013.06.007
|
| [8] |
Rödelsperger S, Becker M, Gerstenecker C, et al. Digital Elevation Model with the Ground-based SAR IBIS-L as Basis for Volcanic Deformation Monitoring[J]. Journal of Geodynamics, 2010, 49(3-4): 241-246. doi: 10.1016/j.jog.2009.10.009
|
| [9] |
Casagli N, Catani F, Del Ventisette C, et al. Monitoring, Prediction, and Early Warning Using Ground-based Radar Interferometry[J]. Landslides, 2010, 7: 291-301. doi: 10.1007/s10346-010-0215-y
|
| [10] |
Lombardi L, Nocentini M, Frodella W, et al. The Calatabiano Landslide (Southern Italy): Preliminary GB-InSAR Monitoring Data and Remote 3D Mapping[J]. Landslides, 2017, 14: 685-696. doi: 10.1007/s10346-016-0767-6
|
| [11] |
Li Y, Jiao Q, Hu X, et al. Detecting the Slope Movement After the 2018 Baige Landslides Based on Ground-based and Space-borne Radar Observations[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 84: 101949. doi: 10.1016/j.jag.2019.101949
|
| [12] |
Qiu Z, Jiao M, Jiang T, et al. Dam Structure Deformation Monitoring by GB-InSAR Approach[J]. IEEE Access, 2020, 8: 123287-123296. doi: 10.1109/ACCESS.2020.3005343
|
| [13] |
Barla M, Antolini F. Monitoring Geotechnical Structures by Ground-based Radar Interferometry[C]//The 7th International Conference on Structural Health Monitoring of Intelligent Infrastractures, Torino, Italy, 2015.
|
| [14] |
Van Leijen F J, Hanssen R F. Persistent Scatterer Density Improvement Using Adaptive Deformation Models[C]//IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 2007.
|
| [15] |
Sousa J J, Hooper A J, Hanssen R F, et al. Persistent Scatterer InSAR: A Comparison of Methodologies Based on a Model of Temporal Deformation vs. Spatial Correlation Selection Criteria[J]. Remote Sensing of Environment, 2011, 115(10): 2652-2663. doi: 10.1016/j.rse.2011.05.021
|
| [16] |
Tofani V, Raspini F, Catani F, et al. Persistent Scatterer Interferometry (PSI) Technique for Landslide Characterization and Monitoring[J]. Remote Sensing, 2013, 5(3): 1045-1065. doi: 10.3390/rs5031045
|
| [17] |
Ferretti A, Prati C, Rocca F. Permanent Scatterers in SAR Interferometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(1): 8-20. doi: 10.1109/36.898661
|
| [18] |
Berardino P, Fornaro G, Lanari R, et al. A New Algorithm for Surface Deformation Monitoring Based on Small Baseline Differential SAR Interferograms[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(11): 2375-2383. doi: 10.1109/TGRS.2002.803792
|
| [19] |
Adam N, Kampes B, Eineder M. Development of a Scientific Permanent Scatterer System: Modifications for Mixed ERS/Envisat Time Series[C]//The 2004 Envisat & ERS Symposium (ESA SP-572), Salzburg, Austria, 2004.
|
| [20] |
Hooper A J. Persistent Scatter Radar Interferometry for Crustal Deformation Studies and Modeling of Volcanic Deformation[D]. Stanford : Stanford University, 2006.
|
| [21] |
Rödelsperger S. Real-time Processing of Ground-based Synthetic Aperture Radar (GB-SAR) Measurements[D]. Darmstadt: Technische Universität, 2011.
|
| [22] |
陈强. 基于永久散射体雷达差分干涉探测区域地表形变的研究[D]. 成都: 西南交通大学, 2006.
Chen Qiang. Detection Regional Groud Deformation by Differential SAR Interferometry Based on Permanent Scatterers[D]. Chengdu: Southwest Jiaotong University, 2006.
|
| [23] |
Yang Y, Wan Y, Wan Q. A 2D-PRM Based Atmospheric Phase Correction Method in GB-SAR Interferometry Application[J]. IEEE Sensors Letters, 2023, 7(6): 23163289.
|
| [24] |
Yang H, Liu Y, Fan H, et al. Identification and Analysis of Deformation Areas in the Construction Stage of Pumped Storage Power Station Using GB-InSAR Technology[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16: 23244936.
|
| [25] |
Tang J, Liu Y, Jin C, et al. Highly Coherent Points Selection Method in Complex Environment[C]//The 7th Asia Pacific Conference on Optics Manufacture and 2021 International Forum of Young Scientists on Advanced Optical Manufacturing (APCOM and YSAOM 2021), HongKong, China, 2022.
|
| [26] |
Wang Y, Lü S, Bai Z, et al. Research on GBSAR Deformation Extraction Method Based on Three-Threshold[C]//The 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China, 201.
|
| [27] |
陈强, 刘国祥, 李永树, 等. 干涉雷达永久散射体自动探测——算法与实验结果[J]. 测绘学报, 2006, 35(2): 112-117. doi: 10.3321/j.issn:1001-1595.2006.02.004
Chen Qiang, Liu Guoxiang, Li Yongshu, et al. Automated Detection of Permanent Scatterers in Radar Interferometry: Algorithm and Testing Results[J]. Acta Geodaetica et Cartographica Sinica, 2006, 35(2): 112-117. doi: 10.3321/j.issn:1001-1595.2006.02.004
|
| [28] |
卢丽君, 廖明生, 王腾, 等. 一种在长时间序列SAR影像上提取稳定目标点的多级探测法[J]. 遥感学报, 2008, 12(4): 561-567. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200804004.htm
Lu Lijun, Liao Mingsheng, Wang Teng, et al. A Multi-Step Detection Method for Extraction of Stable Pointwisetarget in Long Temporal SAR Image Series[J]. National Remote Sensing Bulletin, 2008, 12(4): 561-567. https://www.cnki.com.cn/Article/CJFDTOTAL-YGXB200804004.htm
|
| [29] |
Cao W, Mao Y, Wu L, et al. Research on a GB-SAR Interferogram Filtering Method for Open-Pit Mines Based on Eight-Neighbourhood Outlier Discrimination[J]. Geocarto International, 2022, 37(26): 12219-12237. doi: 10.1080/10106049.2022.2066199
|
| [30] |
Xiang X, Chen C, Wang H, et al. A Real-time Processing Method for GB-SAR Monitoring Data by Using the Dynamic Kalman Filter Based on the PS Network[J]. Landslides, 2023, 20(1): 1-17. doi: 10.1007/s10346-022-01960-1
|
| [31] |
Spaans K, Hooper A. InSAR Processing for Volcano Monitoring and Other Near‐Real Time Applications[J]. Journal of Geophysical Research: Solid Earth, 2016, 121(4): 2947-2960. doi: 10.1002/2015JB012752
|
| [32] |
Crosetto M, Biescas E, Duro J, et al. Generation of Advanced ERS and Envisat Interferometric SAR Products Using the Stable Point Network Technique[J]. Photogrammetric Engineering & Remote Sensing, 2008, 74(4): 443-450.
|
| [33] |
Wang Z, Li Z, Mills J. A New Approach to Selecting Coherent Pixels for Ground-based SAR Deformation Monitoring[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144: 412-422. doi: 10.1016/j.isprsjprs.2018.08.008
|
| [34] |
Crosetto M, Monserrat O, Luzi G, et al. Deformation Monitoring Using Ground-based SAR Data[C]//Engineering Geology for Society and Territory-Volume 5: Urban Geology, Sustainable Planning and Landscape Exploitation, Cham: Springer, 2015.
|
| [35] |
Wang Z, Li Z, Mills J P. A New Nonlocal Method for Ground-based Synthetic Aperture Radar Deformation Monitoring[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(10): 3769-3781. doi: 10.1109/JSTARS.2018.2864740
|
| [36] |
Long S, Tong A, Yuan Y, et al. New Approaches to Processing Ground-based SAR (GBSAR) Data for Deformation Monitoring[J]. Remote Sensing, 2018, 10(12): 1936. doi: 10.3390/rs10121936
|
| [37] |
Costantini M, Rosen P A. A Generalized Phase Unwrapping Approach for Sparse Data[C]//IEEE International Geoscience and Remote Sensing Symposium, Hamburg, Germany, 1999.
|
| [38] |
李兵权, 李永生, 姜文亮, 等. 基于地基真实孔径雷达的边坡动态监测研究与应用[J]. 武汉大学学报(信息科学版), 2019, 44(7): 1093-1098. doi: 10.13203/j.whugis20190049
Li Bingquan, Li Yongsheng, Jiang Wenliang, et al. Research and Application of Slope Dynamic Monitoring Based on Ground-based Real Aperture Radar[J]. Geomatics and Information Science of Wuhan University, 2019, 44(7): 1093-1098. doi: 10.13203/j.whugis20190049
|
| [39] |
Pipia L, Fabregas X, Aguasca A, et al. Atmospheric Artifact Compensation in Ground-based DInSAR Applications[J]. IEEE Geoscience and Remote Sensing Letters, 2008, 5(1): 88-92. doi: 10.1109/LGRS.2007.908364
|
| [40] |
Perski Z, Hanssen R, Wojcik A, et al. InSAR Analyses of Terrain Deformation Near the Wieliczka Salt Mine, Poland[J]. Engineering Geology, 2009, 106(1-2): 58-67. doi: 10.1016/j.enggeo.2009.02.014
|
| [41] |
Deledalle C A, Denis L, Tupin F. NL-InSAR: Nonlocal Interferogram Estimation[J]. IEEE Tran-sactions on Geoscience and Remote Sensing, 2010, 49(4): 1441-1452.
|
| [1] | HE Chaoyang, XU Qiang, JU Nengpan, XIE Mingli. Optimization of Model Scheduling Algorithm in Real-Time Monitoring and Early Warning of Landslide[J]. Geomatics and Information Science of Wuhan University, 2021, 46(7): 970-982. DOI: 10.13203/j.whugis20200314 |
| [2] | XU Zheng, ZOU Bin, ZHENG Zhong, PU Qiang, YANG Zhonglin, SUN Guoqing. A Dynamic Healthy-Route Search Algorithm and System Realization[J]. Geomatics and Information Science of Wuhan University, 2019, 44(1): 145-152. DOI: 10.13203/j.whugis20150749 |
| [3] | ZHU Qing, HAN Huipeng, YU Jie, DU Zhiqiang, ZHANG Junxiao, WU Chen, SHEN Fuqiang. Multi-objective Optimization Scheduling Method for UAV Resources in Emergency Surveying and Mapping[J]. Geomatics and Information Science of Wuhan University, 2017, 42(11): 1608-1615. DOI: 10.13203/j.whugis20130000 |
| [4] | ZHANG Dengyi, GUO Lei, WANG Qian, ZOU Hua. An Improved Single-orbit Scheduling Method for Agile ImagingSatellite Towards Area Target[J]. Geomatics and Information Science of Wuhan University, 2014, 39(8): 901-905. DOI: 10.13203/j.whugis20130233 |
| [5] | DONG Jian, PENG Rencan, ZHENG Yidong. An Improved Algorithm of Point by Point Interpolation by Using Local Dynamic Optimal Delaunay Triangulation Network[J]. Geomatics and Information Science of Wuhan University, 2013, 38(5): 613-617. |
| [6] | TU Wei, LI Qingquan, FANG Zhixiang. A Heuristic Algorithm for Large Scale Vehicle Routing Problem[J]. Geomatics and Information Science of Wuhan University, 2013, 38(3): 307-310. |
| [7] | SONG Huanhuan, WANG Shuzong. Application of Information Entropy Theory to Analyzing Tasks Scheduling for Submarine Combat System[J]. Geomatics and Information Science of Wuhan University, 2012, 37(12): 1477-1481. |
| [8] | YANG Chuncheng, XIE Peng, HE Liesong, ZHOU Xiaodong. Data Scheduling for Map Data Reading[J]. Geomatics and Information Science of Wuhan University, 2009, 34(2): 166-169. |
| [9] | Li Mingshan, Lu Zhiyan. The Generalized Backtracking Method & the Optimum Task Scheduling of the Parallel Computer System[J]. Geomatics and Information Science of Wuhan University, 1996, 21(1): 90-95. |
| [10] | Zheng Zhaobao. An Automatic Searching Method of the Image Occlusion[J]. Geomatics and Information Science of Wuhan University, 1988, 13(4): 71-75. |
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: