编队飞行InSAR的平地效应与地形高度效应分析

在编队飞行条件下,推导了InSAR干涉相位特性与平地之间的关系以及相位特性与地形高度之间的关系,定量分析了平地效应对相位的影响以及地形高度对相位的影响.平地与地形高度二者共同影响了InSAR信号的相位,因此相位表达式可以写成二个偏导数的和的形式.以圆锥体为例仿真了地面场景,在不同视角,不同雷达波长,不同基线长度的条件下做了仿真,仿真实验的结果与理论分析的结果一致.     

Quality assessment of SRTM C- and X-band interferometric data: Implications for the retrieval of vegetation canopy height

The Shuttle Radar Topography Mission distinguished itself as the first near-global spaceborne mission to demonstrate direct sensitivity to vertical vegetation structure. Whether this sensitivity is viewed as exploitable signal or unwanted bias, a great deal of interest exists in retrieving vegetation canopy height information from the SRTM data. This study presents a comprehensive application-specific assessment of SRTM data quality, focusing on the characterization and mitigation of two primary sources of relative vertical error: uncompensated Shuttle mast motion and random phase noise. The assessment spans four test sites located in the upper Midwestern United States and examines the dependence of data quality on both frequency, i.e., C-band vs. X-band, and the number of acquired datatakes. The results indicate that the quality of SRTM data may be higher than previously thought. Novel mitigation strategies include a knowledge-based approach to sample averaging, which has the potential to reduce phase noise error by 43 to 80%. The strategies presented here are being implemented as part of an ongoing effort to produce regional- to continental-scale estimates of vegetation canopy height within the conterminous U.S.     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

一种新的InSAR基线估计方法

针对InSAR技术中基线估计误差对DEM精度的影响,从传统的基线估计方法入手,提出了一种新的估计基线参数的方法,并用仿真实验做了论证.结果表明,该方法比以往传统的方法得到的基线参数的精度更高,运算速度更快,应用范围更加广泛.     

InSAR中应用可靠度导向改进的分级表相位展开算法

目的 在干涉合成孔径雷达的相位展开过程中,如果相位展开的展开路径穿过残差点就会产生相位展开的误差。在相位展开的时候,希望将相位展开的误差限制在局部区域,提高相位展开的精度。方法 首先通过判断截断相位图的残差点,并以InSAR的干涉一致性图作为相位展开的可靠性度量,对截断相位图进行量化,将残差处的可靠度设为排序中的最低等级。在进行可靠度排序后进行干涉合成孔径雷达相位图的相位展开。结果 使用该算法改进了经典可靠度导向相位展开算法中可靠度排序的缓慢问题。与经典菱形算法相比,展开精度高很多。与洪水算法相比,展开速度能快上百倍。通过可靠度排序的改进,对干涉合成孔径雷达相位展开后的结果进行分析,可以看到提高了相位展开的精度,有效的控制了相位展开过程中误差的传递。结论 相位展开算法的改进提高了干涉合成孔径雷达相位展开的精度,在干涉合成孔径雷达的相位展开中可以广泛应用。     

InSAR两种相位解缠算法的融合

相位解缠是合成孔径雷达（SAR）干涉测量数据处理的重要步骤之一。解缠的精度将直接影响干涉测量结果的精度,本文通过实验研究,对比分析了现有的两类主要的相位解缠算法的解缠原理、解缠思想、各自的特点及其引起误差的原因,然后结合各自的优点组成了一种效果更好的二维相位解缠算法。使用该算法解缠后的相位条纹与原始干涉相位图相位条纹的一致性非常好。     

星载InSAR非相干图像对精确配准

为了解决星载InSAR非相干图像配准中不同区域对应不同形变参数的问题,将几种传统的SAR图像配准和光学图像配准的方法有机结合,提出了一种基于概略配准、Harris角点检测、相关系数匹配、Delaunay三角网剖分插值映射的InSAR复图像精确配准方法。通过仿真验证,该方法能够适用相干和非相干的InSAR复图像。配准精度达到0.1像素级。     

结合全局上下文与融合注意力的干涉相位去噪

目的 干涉相位去噪是合成孔径雷达干涉测量（interferometric synthetic aperture radar,InSAR）技术中的关键环节,其效果对测量精度具有重要影响。针对现有的干涉相位去噪方法大多关注局部特征以及在特征提取方面的局限性,同时为了平衡去噪和结构保持两者之间的关系,提出了一种结合全局上下文与融合注意力的相位去噪网络GCFA-PDNet(global context and fused attention phase denoising network)。方法 将干涉相位分离为实部和虚部依次输入到网络,先从噪声相位中提取浅层特征,再将其映射到由全局上下文提取模块和融合注意力模块组成的特征增强模块,最后通过全局残差学习生成去噪图像。全局上下文提取模块能提取全局上下文信息,具有非局部方法的优势;融合注意力模块既强调关键特征,又能高效提取隐藏在复杂背景中的噪声信息。结果 所提出的方法与对比方法中性能最优者相比,在模拟数据结果的平均峰值信噪比（peak signal to noise ratio, PSNR）和结构相似性（structural similarity,...     

昆仑山口西8.1级大地震震后库赛湖地区多时相InSAR地表形变遥感探测

利用2003年~2007年间14景欧空局ENVISAT ASAR卫星数据,结合SRTM DEM数据,采用多时相SAR协同配准方法进行二路干涉处理,对2001年11月昆仑山口西8.1级大地震宏观震中—库赛湖地区的地表形变场进行了多时相探测。结果表明:①多时相InSAR技术能够对位于高海拔山区的东昆仑断裂带库赛湖段的地表形变实施连续监测;②震后发震断裂带的调整活动经历了一个动态变化的过程,2003年~2004年、2005年9月至2006年2月相对活跃,调整幅度较大,2004年11月至2005年9月间调整较小;③2006年~2007年发震断裂带地区活动逐渐减弱。     

Forest biomass mapping from lidar and radar synergies

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed.In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R² of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and was used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R² of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar-SAR synergy was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.