SAR干涉图降噪的稳健协方差矩阵分解法
|
摘要
干涉图降噪在InSAR技术应用中发挥着重要作用,若降噪效果不好将引起干涉图相位解缠的误差,并进一步导致DEM或形变结果的错误。由于干涉图分辨单元的信号(相位)是由分辨单元内多个散射体的回波信号(相位)叠加而成,本文针对单一主导散射体的散射模型(永久性散射体模型)和只考虑一种散射机制的分布式散射体模型相位的特点,对多基线SAR数据估计的协方差矩阵采用特征值分解的方法来分离相位中的噪声,通过提取最大特征值对应的特征向量(相位),从而实现干涉图降噪的目的。而对于协方差矩阵估计时引入的异质点,本文采用了一种稳健的协方差矩阵估计方法。通过覆盖山西清徐地面沉降形变区的8景真实TerraSAR数据试验验证了该方法的有效性。结果表明该方法比改进的Goldstein滤波方法在相干性提高、有效目标点增加两方面均有显著提高,特别在低相干区域由于相干点的增加也获取了更多的形变监测信息。
Interferogram denoising plays an important role to the application of InSAR technique.If the phase noise cannot be well filtered,the phase unwrapping error is frequently arisen,which will further result in the mistakes in the DEM product and the deformation result.The complex value of each SAR resolution unit is superimposed by the phases from different scatterers,so the paper focuses on the characteristics of single dominant phase scattering model(the permanent scatterer)and traditional distributed scatterer of single scattering mechanism.Then the robust covariance matrix,estimated based on multi-baseline SAR data,is decomposed and the eigenvector corresponding to the maximum eigenvalue is chosen as the filtered phase.Besides,the covariance matrix is robustly estimated by weighted averaging the heterogeneous points.This method can operate better than the improved Goldstein filter algorithm in the terms of coherence improvement and effective coherent targets increasing,especially in the low-coherence regions.Eight real TerraSAR-X data over one land subsidence region,Qingxu,Shanxi verifies the advantages of our new method.
Interferogram denoising plays an important role to the application of InSAR technique.If the phase noise cannot be well filtered,the phase unwrapping error is frequently arisen,which will further result in the mistakes in the DEM product and the deformation result.The complex value of each SAR resolution unit is superimposed by the phases from different scatterers,so the paper focuses on the characteristics of single dominant phase scattering model(the permanent scatterer)and traditional distributed scatterer of single scattering mechanism.Then the robust covariance matrix,estimated based on multi-baseline SAR data,is decomposed and the eigenvector corresponding to the maximum eigenvalue is chosen as the filtered phase.Besides,the covariance matrix is robustly estimated by weighted averaging the heterogeneous points.This method can operate better than the improved Goldstein filter algorithm in the terms of coherence improvement and effective coherent targets increasing,especially in the low-coherence regions.Eight real TerraSAR-X data over one land subsidence region,Qingxu,Shanxi verifies the advantages of our new method.
引文
[1] REES W G,SATCHELL M J F.The effect of median filtering on synthetic aperture radar images[J].International Journal of Remote Sensing,1997,18(13):2887-2893.
[2] DELEDALLE C A,DENIS L,TUPIN F.NL-InSAR:nonlocal interferogram estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(4):1441-1452.
[3] GOLDSTEIN R M,WERNER C L.Radar interferogram filtering for geophysical applications[J].Geophysical Research Letters,1998,25(21):4035-4038.
[4] LOPEZ-MARTINEZ C,FABREGAS X.Modeling and reduction of SAR interferometric phase noise in the wavelet domain[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(12):2553-2566.
[5] BARAN I,STEWART M P,KAMPES B M,et al.A modification to the Goldstein radar interferogram filter[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(9):2114-2118.
[6] LI Zhiwei,DING Xiaoli,HUANG C,et al.Improved filtering parameter determination for the Goldstein radar interferogram filter[J].ISPRS Journal of Photogrammetry and Remote Sensing,2008,63(6):621-634.
[7] ZHAO Chaoying,ZHANG Qin,DING Xiaoli,et al.An iterative Goldstein SAR interferogram filter[J].International Journal of Remote Sensing,2012,33(11):3443-3455.
[8]赵文胜,蒋弥,何秀凤.干涉图像第二类统计Goldstein自适应滤波方法[J].测绘学报,2016,45(10):1200-1209.DOI:10.11947/j.AGCS.2016.20150457.ZHAO Wensheng,JIANG Mi,HE Xiufeng.Improved adaptive Goldstein interferogram filter based on second kind statistics[J].Acta Geodaetica et Cartographica Sinica,2016,45(10):1200-1209. DOI:10. 11947/j. AGCS.2016.20150457.
[9] FERRETTI A,FUMAGALLI A,NOVALI F,et al.A new algorithm for processing interferometric data-stacks:SqueeSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(9):3460-3470.
[10] GOEL K,ADAM N.An advanced algorithm for deformation estimation in non-urban areas[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73:100-110.
[11] ZHANG Zhengjia,TANG Yixian,ZHANG Hong,et al.Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers[C]∥Proceedings of 2014IEEE Geoscience and Remote Sensing Symposium.Quebec City, Canada:IEEE,2014:433-436.
[12]王明洲,李陶,江利明,等.地表形变监测的改进相干目标法[J].测绘学报,2016,45(1):36-43.WANG Mingzhou,LI Tao,JIANG Liming,et al.An improved coherent targets technology for monitoring surface deformation[J].Acta Geodaetica et Cartographica Sinica,2016,45(1):36-43.
[13] FORNARO G,VERDE S,REALE D,et al.CAESAR:an approach based on covariance matrix decomposition to improve multibaseline-multitemporal interferometric SAR processing[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(4):2050-2065.
[14] BAMLER R,HARTL P.Synthetic aperture radar interferometry[J].Inverse Problems,1998,14(4):1-54.
[15] GOEL K,ADAM N.High resolution deformation time series estimation for distributed scatterers using TerraSAR-X data[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,I-7:29-34.
[16] JIANG Mi,DING Xiaoli,HANSSEN R F,et al.Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(3):1213-1224.
[17] SCHMITT M,SCHNBERGER J L,STILLA U.Adaptive covariance matrix estimation for multi-baseline InSAR data stacks[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(11):6807-6817.
[18] WANG Yuanyuan,ZHU Xiaoxiang.Robust estimators for multipass SAR interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(2):968-980.
[19] OLLILA E,KOIVUNEN V.Influence functions for array covariance matrix estimators[C]∥Proceedings of 2003IEEE Workshop on Statistical Signal Processing.St Louis,MO:IEEE,2003:462-465.
[20] VISURI S,KOIVUNEN V,OJA H.Sign and rank covariance matrices[J].Journal of Statistical Planning and Inference,2000,91(2):557-575.
[21] CAO Ning,LEE H,JUNG H C.A phase-decompositionbased PSInSAR processing method[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(2):1074-1090.
[22] ZHANG Qin,ZHU Wu,DING Xiaoli,et al.Two-dimensional deformation monitoring over Qingxu(China)by integrating C-,L-and X-bands SAR images[J].Remote Sensing Letters,2014,5(1):27-36.
[23]赵超英,张勤,张静.山西清徐地裂缝形变的InSAR监测分析[J].工程地质学报,2011,19(1):70-75.ZHAO Chaoying,ZHANG Qin,ZHANG Jing.Deformation monitoring of ground fissure with SAR interferometry in Qingxu,Shanxi Province[J].Journal of Engineering Geology,2011,19(1):70-75.
[24]门玉明,彭建兵,李寻昌.山西清徐县地裂缝灾害现状及类型分析[J].工程地质学报,2007,15(4):453-457.MEN Yuming,PENG Jianbing,LI Xunchang.Present status and classification of ground fissure hazards in Qingxu County Shanxi Province[J]. Journal of Engineering Geology,2007,15(4):453-457.
[25] LI Zhilin,ZOU Weibao,DING Xiaoli,et al.A quantitative measure for the quality of InSAR interferograms based on phase differences[J].Photogrammetric Engineering&Remote Sensing,2004,70(10):1131-1137.
[2] DELEDALLE C A,DENIS L,TUPIN F.NL-InSAR:nonlocal interferogram estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(4):1441-1452.
[3] GOLDSTEIN R M,WERNER C L.Radar interferogram filtering for geophysical applications[J].Geophysical Research Letters,1998,25(21):4035-4038.
[4] LOPEZ-MARTINEZ C,FABREGAS X.Modeling and reduction of SAR interferometric phase noise in the wavelet domain[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(12):2553-2566.
[5] BARAN I,STEWART M P,KAMPES B M,et al.A modification to the Goldstein radar interferogram filter[J].IEEE Transactions on Geoscience and Remote Sensing,2003,41(9):2114-2118.
[6] LI Zhiwei,DING Xiaoli,HUANG C,et al.Improved filtering parameter determination for the Goldstein radar interferogram filter[J].ISPRS Journal of Photogrammetry and Remote Sensing,2008,63(6):621-634.
[7] ZHAO Chaoying,ZHANG Qin,DING Xiaoli,et al.An iterative Goldstein SAR interferogram filter[J].International Journal of Remote Sensing,2012,33(11):3443-3455.
[8]赵文胜,蒋弥,何秀凤.干涉图像第二类统计Goldstein自适应滤波方法[J].测绘学报,2016,45(10):1200-1209.DOI:10.11947/j.AGCS.2016.20150457.ZHAO Wensheng,JIANG Mi,HE Xiufeng.Improved adaptive Goldstein interferogram filter based on second kind statistics[J].Acta Geodaetica et Cartographica Sinica,2016,45(10):1200-1209. DOI:10. 11947/j. AGCS.2016.20150457.
[9] FERRETTI A,FUMAGALLI A,NOVALI F,et al.A new algorithm for processing interferometric data-stacks:SqueeSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2011,49(9):3460-3470.
[10] GOEL K,ADAM N.An advanced algorithm for deformation estimation in non-urban areas[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 73:100-110.
[11] ZHANG Zhengjia,TANG Yixian,ZHANG Hong,et al.Subsidence monitoring in coal area using time-series InSAR combining persistent scatterers and distributed scatterers[C]∥Proceedings of 2014IEEE Geoscience and Remote Sensing Symposium.Quebec City, Canada:IEEE,2014:433-436.
[12]王明洲,李陶,江利明,等.地表形变监测的改进相干目标法[J].测绘学报,2016,45(1):36-43.WANG Mingzhou,LI Tao,JIANG Liming,et al.An improved coherent targets technology for monitoring surface deformation[J].Acta Geodaetica et Cartographica Sinica,2016,45(1):36-43.
[13] FORNARO G,VERDE S,REALE D,et al.CAESAR:an approach based on covariance matrix decomposition to improve multibaseline-multitemporal interferometric SAR processing[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(4):2050-2065.
[14] BAMLER R,HARTL P.Synthetic aperture radar interferometry[J].Inverse Problems,1998,14(4):1-54.
[15] GOEL K,ADAM N.High resolution deformation time series estimation for distributed scatterers using TerraSAR-X data[J].ISPRS Annals of Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,I-7:29-34.
[16] JIANG Mi,DING Xiaoli,HANSSEN R F,et al.Fast statistically homogeneous pixel selection for covariance matrix estimation for multitemporal InSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(3):1213-1224.
[17] SCHMITT M,SCHNBERGER J L,STILLA U.Adaptive covariance matrix estimation for multi-baseline InSAR data stacks[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(11):6807-6817.
[18] WANG Yuanyuan,ZHU Xiaoxiang.Robust estimators for multipass SAR interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(2):968-980.
[19] OLLILA E,KOIVUNEN V.Influence functions for array covariance matrix estimators[C]∥Proceedings of 2003IEEE Workshop on Statistical Signal Processing.St Louis,MO:IEEE,2003:462-465.
[20] VISURI S,KOIVUNEN V,OJA H.Sign and rank covariance matrices[J].Journal of Statistical Planning and Inference,2000,91(2):557-575.
[21] CAO Ning,LEE H,JUNG H C.A phase-decompositionbased PSInSAR processing method[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(2):1074-1090.
[22] ZHANG Qin,ZHU Wu,DING Xiaoli,et al.Two-dimensional deformation monitoring over Qingxu(China)by integrating C-,L-and X-bands SAR images[J].Remote Sensing Letters,2014,5(1):27-36.
[23]赵超英,张勤,张静.山西清徐地裂缝形变的InSAR监测分析[J].工程地质学报,2011,19(1):70-75.ZHAO Chaoying,ZHANG Qin,ZHANG Jing.Deformation monitoring of ground fissure with SAR interferometry in Qingxu,Shanxi Province[J].Journal of Engineering Geology,2011,19(1):70-75.
[24]门玉明,彭建兵,李寻昌.山西清徐县地裂缝灾害现状及类型分析[J].工程地质学报,2007,15(4):453-457.MEN Yuming,PENG Jianbing,LI Xunchang.Present status and classification of ground fissure hazards in Qingxu County Shanxi Province[J]. Journal of Engineering Geology,2007,15(4):453-457.
[25] LI Zhilin,ZOU Weibao,DING Xiaoli,et al.A quantitative measure for the quality of InSAR interferograms based on phase differences[J].Photogrammetric Engineering&Remote Sensing,2004,70(10):1131-1137.