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基于Kalman滤波的InSAR基线估计方法
引用本文:何敏,何秀凤.基于Kalman滤波的InSAR基线估计方法[J].遥感学报,2008,12(1):23-27.
作者姓名:何敏  何秀凤
作者单位:河海大学,卫星及空间信息研究所,江苏,南京,210098
基金项目:国家自然科学基金 , 现代水利科技创新项目 , 江苏省研究生培养创新工程项目
摘    要:针对目前SAR干涉测量中基线估计现存的问题,提出了利用Kalman滤波和配准参数进行基线估计的方法.所提出的方法具有不需地面控制点、不受地形限制和不依赖于轨道参数等优点,并可以估计时变的基线参数.利用南京地区的ERS-1/2 tandem数据进行了试验研究,并对提出的方法进行了验证.结果表明,在精确的卫星轨道数据和地面控制点不能获取时,所提出的方法仍能有效地估计InSAR基线.这在一定程度上补偿了轨道偏移带来的误差,为获取高精度的DEM奠定了基础.

关 键 词:Kalman滤波  InSAR  基线估计  配准  Kalman  Filter  滤波  InSAR  基线估计  估计方法  Estimation  Method  精度  误差  轨道数据  补偿  程度  卫星  结果  验证  研究  试验  tandem  南京地区  时变  轨道参数
文章编号:1007-4619(2008)01-0023-05
修稿时间:2006年10月11

An InSAR Baseline Estimation Method Using Kalman Filter
HE Min and HE Xiu-feng.An InSAR Baseline Estimation Method Using Kalman Filter[J].Journal of Remote Sensing,2008,12(1):23-27.
Authors:HE Min and HE Xiu-feng
Abstract:Interferometric Synthetic aperture radar(InSAR) is based on the concept of observing the same scene with two slightly different radar trajectories.Each point of the scene is seen from two different positions along the two radar trajectories,and the differences between those points constitute the interferometric baseline.For quantitative SAR interferometry and differential interferometry applications,e.g.building digital elevation models(DEMs) or monitoring terrain displacement,the baseline must be estimated with a higher accuracy than generally achieved from satellite or airplane trajectories or attitude measurements.Traditional techniques for accurate baseline estimation need a priori information,like orbit data(to be known with a high precision),fringe frequencies in flat areas,ground control points(GCP) and existing digital elevation models(DEM).In many cases it is not possible to meet these requirements,i.e.neither orbit data or a digital elevation models(DEM) are available nor Ground Control Points(GCP) or flat terrain are within the scene.In this paper,a method of the InSAR baseline estimation is proposed based on Kalman filter and co-registration parameters of the interferometric SAR images and is not limited to the restrictions mentioned above.The proposed method are free from the following restrictions: 1.ground control points(GCPs);2.existing digital elevation model(DEM);3.flat terrain in the scenes;4.independence of orbit data.Moreover,the novel method allows the determination of time varying baseline parameters and is independent of the accurate knowledge of the height of the tie point.So the method can be applied when precise orbit data,ground control points(GCPs) or existing digital elevation models(DEM) are unavailable,or in mountainous regions where flat areas are difficult to be observed in the scene.The proposed method is made up of two steps: obtaining co-registration parameters of the interferometric SAR images and estimating the InSAR baseline based on Kalman filter.In the first step,the co-registration accuracy of the interferometric SAR images is required in 0.1pixels.In the second step,a geometrical relation between the pixel offset in range and the baseline components is derived,and the initial values of the baseline parameters are calculated using the forecasted satellite orbit data.Additional use of the knowledge about the stochastic characteristics of the involved parameters allows the determination of the baseline components by using Kalman filter.In the method a flat Earth approximation was considered,namely considering the terrain height as a constant equalling to the average value of the terrain height in the scene.The proposed method is examined using the ERS-1/2 Tandem data from the European Space Agency(ESA).The data are imaged in Nanjing test site,which is located in the east of China.The results show that the proposed method can obtain the accuracy of baseline parameters at dm level and make up the limitations of baseline estimation when the accurate orbit data and ground control points can not be obtained.And the errors caused by orbit shift have been reduced.Thus,the method proposed can improve the accuracy of the DEM produced.
Keywords:Kalman filter  InSAR  baseline estimation  co-registration
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