顾及时空特性的SBAS高质量点选取算法
|
摘要
SBAS技术中常用的相干系数选点方法受旁瓣效应的影响,容易造成部分高质量点的漏选以及低质量点的误选。针对这一问题,本文提出了顾及时空特性的SBAS高质量点选取算法,根据干涉相位各个成分的空间特性,分离出噪声相位判别点的质量。试验表明考虑同质点的Non-Local滤波能够更加可靠地提取干涉相位中的空间相关相位,从而提高噪声相位估计的准确性。利用覆盖上海地区1992—1998年的24景JERS-1影像作为试验数据,分别用相干系数法以及本文的方法进行选点。结果表明本文算法能够有效地选出相干系数法在农田与村落交错地区漏选的高质量点,同时排除了相干系数法由于旁瓣效应影响的误选点。
Traditional coherence-based point-selection method in the SBAS technique is often suffered from the side lobe effect problem,which can result in the omission of partial high-quality points and reservation of partial low-quality points.A new algorithm to select high-quality points is proposed in the SBAS technique by quantifying the separated noise phase component.Compared with the traditional filtering methods,it is found that the Non-Local algorithm considering the homogeneous points can accurately separate spatially-correlated phase from the unwrapping differential interferometric phase,and then the precision of the noise phase component estimation is improved.Coherence-based approach and our new algorithm are compared by selecting high-quality points and mapping the surface deformation of the test area in Shanghai.There it is utilized the 24JERS-1SAR images acquired from 1992 to 1998.The results suggest that the proposed algorithm not only can select the high-quality points in fields covered by farmlands and hamlets which are omitted by coherence-based method,but also can effectively exclude the points extracted by coherence-based method due to the side lobe effect.
Traditional coherence-based point-selection method in the SBAS technique is often suffered from the side lobe effect problem,which can result in the omission of partial high-quality points and reservation of partial low-quality points.A new algorithm to select high-quality points is proposed in the SBAS technique by quantifying the separated noise phase component.Compared with the traditional filtering methods,it is found that the Non-Local algorithm considering the homogeneous points can accurately separate spatially-correlated phase from the unwrapping differential interferometric phase,and then the precision of the noise phase component estimation is improved.Coherence-based approach and our new algorithm are compared by selecting high-quality points and mapping the surface deformation of the test area in Shanghai.There it is utilized the 24JERS-1SAR images acquired from 1992 to 1998.The results suggest that the proposed algorithm not only can select the high-quality points in fields covered by farmlands and hamlets which are omitted by coherence-based method,but also can effectively exclude the points extracted by coherence-based method due to the side lobe effect.
引文
[1]GABRIEL A K,GOLDSTEIN R M,ZEBKER H A.Mapping Small Elevation Changes over Large Areas:Differential Radar Interferometry[J].Journal of Geophysical Research:Solid Earth(1978—2012),1989,94(B7):9183-9191.
[2]LIAO Mingsheng,LIN Hui.Synthetic Aperture Radar Interferometry:Principle and Signal Processing[M].Beijing:Surveying and Mapping Press,2003:1-26.(廖明生,林珲.雷达干涉测量学:原理与信号处理基础[M].北京:测绘出版社,2003:1-26.)
[3]LIU Guoxiang,CHEN Qiang,LUO Xiaojun,et al.Principle and Method of Permanent Scatterer Synthetic Aperture Radar Interferometry[M].Beijing:Science Press,2012:10-12.(刘国祥,陈强,罗小军,等.永久散射体雷达干涉理论与方法[M].北京:科学出版社,2012:10-12.)
[4]FERRRTTI A,PRATI C,ROCCA F.Permanent Scatterers in SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(1):8-20.
[5]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.
[6]HOOPER A J.Persistent Scatter Radar Interferometry for Crustal Deformation Studies and Modeling of Volcanic Deformation[D].California:Stanford University,2006:46-54.
[7]ZHANG Yonghong,WU Hong’an,SUN Guangtong.Deformation Model of Time Series Interferometric SAR Technique[J].Acta Geodaetica et Cartographica Sinica,2012,41(6):864-869.(张永红,吴宏安,孙广通.时间序列InSAR技术中的形变模型研究[J].测绘学报,2012,41(6):864-869.)
[8]LI Shanshan,LI Zhiwei,Hu Jun,et al.Investigation of the Seasonal Oscillation of the Permafrost over QinghaiTibet Plateau with SBAS-InSAR Algorithm[J].Chinese Journal of Geophysics,2013,56(5):1476-1486.(李珊珊,李志伟,胡俊,等.SBAS-InSAR技术监测青藏高原季节性冻土形变[J].地球物理学报,2013,56(5):1476-1486.)
[9]YIN Hongjie,ZHU Jianjun,LI Zhiwei,et al.Ground Subsidence Monitoring in Mining Area Using DInSAR SBAS Algorithm[J].Acta Geodaetica et Cartographica Sinica,2011,40(1):52-58.(尹宏杰,朱建军,李志伟,等.基于SBAS的矿区形变监测研究[J].测绘学报,2011,40(1):52-58.)
[10]TIZZANI P,BERARDINO P,CASU F,et al.Surface Deformation of Long Valley Caldera and Mono Basin,California,Investigated with the SBAS-InSAR Approach[J].Remote Sensing of Environment,2007,108(3):277-289.
[11]JIANG Mi,DING Xiaoli,LI Zhiwei.Hybrid Approach for Unbiased Coherence Estimation for Multi-temporal InSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2459-2473.
[12]ZEBKER H A,CH EN K.Accurate Estimation of Correlation in InSAR Observations[J].IEEE Geoscience and Remote Sensing Letters,2005,2(2):124-127.
[13]MORA O,LANARI R,MALLORQUI J J,et al.A New Algorithm for Monitoring Localized Deformation Phenomena Based on Small Baseline Differential SAR Interferograms[C]∥Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium,2002:IGARSS'02.Toronto:IEEE,2002:1237-1239.
[14]HOOPER A.A Multi-temporal InSAR Method Incorporating both Persistent Scatterer and Small Baseline Approaches[J].Geophysical Research Letters,2008,35(16):L16302.
[15]BUADES A,COLL B,MOREL J M.A Non-Local Algorithm for Image Denoising[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition:CVPR 2005.San Diego:IEEE,2005,2:60-65.
[16]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.
[17]HOU Shumin,LI Yourong,LIU Guanglin.A New Method of Detecting Nonlinear for Time Series Based on KS Test[J].Journal of Electronics&Information Technology,2007,29(4):808-810.(侯澍旻,李友荣,刘光临.一种基于KS检验的时间序列非线性检验方法[J].电子与信息学报,2007,29(4):808-810.)
[18]BUADES A,COLL B,MOREL J M.Non-Local Image and Movie Denoising[J].International Journal of Computer Vision,2008,76(2):123-139.
[19]HANSSEN R F.Radar Interferometry:Data Interpretation and Error Analysis[M].Berlin:Springer,2001:197-244.
[20]DAMOAH-AFARI P.Detecting Ground Settlement of Megacities Using InSAR Techniques[D].Hong Kong:Hong Kong Polytechnic University,2009:91-173.
[21]LU Lijun.Method for Land Deformation Detection Based on Time Series SAR Images and Its Application[D].Wuhan:Wuhan University,2008:99-115.(卢丽君.基于时序SAR影像的地表形变检测方法及其应用[D].武汉:武汉大学,2008:99-115.)
[22]ZHOU Hui,FENG Guangcai,LI Zhiwei,et al.The Fault Slip Distribution of the Myanmar Mw 6.8Earthquake Inferred from InSAR Measurements[J].Chinese Journal of Geophysics,2013,56(9):3011-3021.(周辉,冯光财,李志伟,等.利用InSAR资料反演缅甸Mw6.8地震断层滑动分布[J].地球物理学报,2013,56(9):3011-3021.)
[2]LIAO Mingsheng,LIN Hui.Synthetic Aperture Radar Interferometry:Principle and Signal Processing[M].Beijing:Surveying and Mapping Press,2003:1-26.(廖明生,林珲.雷达干涉测量学:原理与信号处理基础[M].北京:测绘出版社,2003:1-26.)
[3]LIU Guoxiang,CHEN Qiang,LUO Xiaojun,et al.Principle and Method of Permanent Scatterer Synthetic Aperture Radar Interferometry[M].Beijing:Science Press,2012:10-12.(刘国祥,陈强,罗小军,等.永久散射体雷达干涉理论与方法[M].北京:科学出版社,2012:10-12.)
[4]FERRRTTI A,PRATI C,ROCCA F.Permanent Scatterers in SAR Interferometry[J].IEEE Transactions on Geoscience and Remote Sensing,2001,39(1):8-20.
[5]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.
[6]HOOPER A J.Persistent Scatter Radar Interferometry for Crustal Deformation Studies and Modeling of Volcanic Deformation[D].California:Stanford University,2006:46-54.
[7]ZHANG Yonghong,WU Hong’an,SUN Guangtong.Deformation Model of Time Series Interferometric SAR Technique[J].Acta Geodaetica et Cartographica Sinica,2012,41(6):864-869.(张永红,吴宏安,孙广通.时间序列InSAR技术中的形变模型研究[J].测绘学报,2012,41(6):864-869.)
[8]LI Shanshan,LI Zhiwei,Hu Jun,et al.Investigation of the Seasonal Oscillation of the Permafrost over QinghaiTibet Plateau with SBAS-InSAR Algorithm[J].Chinese Journal of Geophysics,2013,56(5):1476-1486.(李珊珊,李志伟,胡俊,等.SBAS-InSAR技术监测青藏高原季节性冻土形变[J].地球物理学报,2013,56(5):1476-1486.)
[9]YIN Hongjie,ZHU Jianjun,LI Zhiwei,et al.Ground Subsidence Monitoring in Mining Area Using DInSAR SBAS Algorithm[J].Acta Geodaetica et Cartographica Sinica,2011,40(1):52-58.(尹宏杰,朱建军,李志伟,等.基于SBAS的矿区形变监测研究[J].测绘学报,2011,40(1):52-58.)
[10]TIZZANI P,BERARDINO P,CASU F,et al.Surface Deformation of Long Valley Caldera and Mono Basin,California,Investigated with the SBAS-InSAR Approach[J].Remote Sensing of Environment,2007,108(3):277-289.
[11]JIANG Mi,DING Xiaoli,LI Zhiwei.Hybrid Approach for Unbiased Coherence Estimation for Multi-temporal InSAR[J].IEEE Transactions on Geoscience and Remote Sensing,2014,52(5):2459-2473.
[12]ZEBKER H A,CH EN K.Accurate Estimation of Correlation in InSAR Observations[J].IEEE Geoscience and Remote Sensing Letters,2005,2(2):124-127.
[13]MORA O,LANARI R,MALLORQUI J J,et al.A New Algorithm for Monitoring Localized Deformation Phenomena Based on Small Baseline Differential SAR Interferograms[C]∥Proceedings of the 2002 IEEE International Geoscience and Remote Sensing Symposium,2002:IGARSS'02.Toronto:IEEE,2002:1237-1239.
[14]HOOPER A.A Multi-temporal InSAR Method Incorporating both Persistent Scatterer and Small Baseline Approaches[J].Geophysical Research Letters,2008,35(16):L16302.
[15]BUADES A,COLL B,MOREL J M.A Non-Local Algorithm for Image Denoising[C]∥Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition:CVPR 2005.San Diego:IEEE,2005,2:60-65.
[16]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.
[17]HOU Shumin,LI Yourong,LIU Guanglin.A New Method of Detecting Nonlinear for Time Series Based on KS Test[J].Journal of Electronics&Information Technology,2007,29(4):808-810.(侯澍旻,李友荣,刘光临.一种基于KS检验的时间序列非线性检验方法[J].电子与信息学报,2007,29(4):808-810.)
[18]BUADES A,COLL B,MOREL J M.Non-Local Image and Movie Denoising[J].International Journal of Computer Vision,2008,76(2):123-139.
[19]HANSSEN R F.Radar Interferometry:Data Interpretation and Error Analysis[M].Berlin:Springer,2001:197-244.
[20]DAMOAH-AFARI P.Detecting Ground Settlement of Megacities Using InSAR Techniques[D].Hong Kong:Hong Kong Polytechnic University,2009:91-173.
[21]LU Lijun.Method for Land Deformation Detection Based on Time Series SAR Images and Its Application[D].Wuhan:Wuhan University,2008:99-115.(卢丽君.基于时序SAR影像的地表形变检测方法及其应用[D].武汉:武汉大学,2008:99-115.)
[22]ZHOU Hui,FENG Guangcai,LI Zhiwei,et al.The Fault Slip Distribution of the Myanmar Mw 6.8Earthquake Inferred from InSAR Measurements[J].Chinese Journal of Geophysics,2013,56(9):3011-3021.(周辉,冯光财,李志伟,等.利用InSAR资料反演缅甸Mw6.8地震断层滑动分布[J].地球物理学报,2013,56(9):3011-3021.)