应用多传感器目标跟踪融合技术实现滑坡多点监测数据综合信息的提取

目前的滑坡预报模型只能利用一个关键监测点的监测数据，而不能同时利用多个监测点的监测信息，即只能处理一维时间序列问题.为了解决这个技术难题，本文将滑坡视为一个机动目标，将对滑坡的监测视为对机动目标的跟踪，提出了利用多传感器目标跟踪融合技术来处理滑坡多个点的监测数据的方法，分析了其可行性，并用实例说明了本文提出的方法在实际应用中是有效的.     

A new approach of single epoch GPS posi- tioning for landslide monitoring

Introduction China is a country with many landslides and debris flows. These disasters bring out a large amount of losses of life and property. It is significant to predict landslide incident by monitoring the deformations of these landslides. At past, triangulation and trilateration are traditional tools, but it is very difficult for them to realize real-time monitoring, and it is more dangerous for obser- vation workers when the deformation becomes larger. Because of many advantages such as…     

A Possible Detection of the 26 December 2004 Great Sumatra-Andaman Islands Earthquake with Solution Products of the International GNSS Service

The main goal of this work is to critically review the IGS solution products and Precise Point Positioning (PPP) in order to demonstrate their potential to contribute to studies of large earthquakes such as the one that devastated Southeast Asia on December 26th, 2004. In view of a possible detection of the Mw 9.0 Sumatra-Andaman Islands Earthquake of December 26, 2004, position solutions, ranging from intervals of years to one second, of four International GNSS Service (IGS) stations within 3000 km of the epicenter were examined. The IGS combined, cumulative solution product (IGS04P51), consisting of epoch and station velocity solutions and based on data spans of several years prior to the earthquake, was used as a reference. Four IGS combined weekly position solutions (igs04P1301-4), two weeks before and after the earthquake, were utilized for the weekly solution resolution. PPP static and kinematic solutions with IGS Final combined orbits and clocks were used for the mean daily and instantaneous 5-min and 1-sec epoch solutions, respectively. The most significant changes, detected by both weekly and daily solutions occurred in longitude. The nearest IGS station ntus, about 1000 km east of the epicenter, moved westward about 15 mm, while the more distant Indian station iisc (∼ 2300 km NW from the epicenter), shifted about 15 mm eastward. In spite of position errors caused by interpolation of the 5-min IGS clocks, the 1-sec solutions, based on separate data sets, available only for two stations (iisc, dgar), still showed seismic surface waves, in particular at the Indian station iisc. Precise daily IGS combined polar motion and length-of-day products, after correcting for the atmospheric effects, also likely detected, statistically significant, anomalistic excitations on December 26, 2004 that could be caused by this great earthquake.     

Applications géodésiques du système DORIS à l'Institut géographique national

IGN is in charge of the installation and maintenance of the DORIS orbit determination network. More recently, in collaboration with JPL, precise geodetic computations were performed. The goal of this paper is to recall the various historic contributions of IGN to the DORIS system in their international context and then to describe a new estimation technique developed for a multi-satellite mode, making full profit of a better modeling for satellites and ground clocks as well as tropospheric correction parameters. Derived geodetic results demonstrate a precision in the order of 1 cm for station positions. To cite this article: P. Willis et al., C. R. Geoscience 337 (2005).     

高分二号卫星影像融合方法探析

图像融合技术是卫星影像处理过程中的一个重要步骤,它可以将全色影像的高空间分辨率特点与多光谱影像的高光谱分辨率特点有机地结合起来,使得融合后的影像更利于后续解译。由于融合处理方法的原理以及获取影像的传感器特性不同,不同的卫星影像适合于不同的融合方法。针对高分二号卫星影像的特点,本文采用Gram-Schmidt融合法、FIHS变换方法、HPF方法、Pansharp方法进行试验。实验结果表明:HPF方法所得融合结果在光谱特性保持方法性能最优,而Pansharp方法融合结果在信息量的保持以及清晰度方面效果更优,因此,在实际应用中,应根据具体需求进行方法选择。     

PPP/SINS紧耦合系统仿真的研究与分析

在捷联惯导(SINS)和GPS卫星接收机进行紧耦合的研究中,采用差分定位进行紧耦合的方法比较成熟,而关于精密单点定位(PPP)与捷联惯导紧耦合的研究还比较少。本文对精密单点定位与捷联惯导紧组合系统进行了Matlab仿真,利用数学解析的方法产生机载运动轨迹,通过设置系统的参数,获得仿真SINS和GPS数据;然后,通过PPP/SINS紧组合系统的仿真程序解算,将定位结果与PPP的结果比较,表明PPP/SINS紧组合导航定位的结果比PPP的精度和可靠性好,而且收敛的速度更快,同时也验证了算法的正确性。最后,分析了不同等级惯导对定位精度的影响。     

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

In this study, the multi-resolution Kalman filter (MKF) algorithm, which can handle multi-resolution problems with high computational efficiency, was used to blend two emissivity products: the Global LAnd Surface Satellite (GLASS) (BBE) product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) narrowband emissivity (NBE) product. The ASTER NBE product was first converted into a BBE product. A new detrending method was used to transfer the BBEs into a process suitable for the MKF. The new detrending method was superior to the two existing methods. Finally, both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale. Field measurements collected in North America were used to validate the integrated BBEs. Visually, the fusion map showed good continuity, with the exception of the border areas, and the quality of the fusion map was better than that of the original maps. The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale. In addition, the MKF was capable of recovering missing pixels at a finer scale.     

GNSS相应坐标系之间的关系及其影响

不同的GNSS采用的坐标系定义几乎相近,但参考椭球及其坐标实现不同,这将影响多GNSS融合导航定位效果。根据各GNSS坐标系所采用参考椭球的基本常数,计算比较了不同坐标系参考椭球参数的差异;导出了相应的正常重力公式,比较了这些正常重力公式确定的正常重力值差异;最后分别从坐标系统的定义与实现两个方面分析了其对定位结果的影响。结果表明:1)GPS(BDS)与Galileo和GLONASS所使用的参考椭球引起正常重力差约为0.15和0.30 mgal;2)GPS与BDS,Galileo及GLONASS所使用参考椭球引起纬度分量最大差异约为0.1 mm,3 cm和3 cm,高程分量约为0.1 mm,0.5 m和1 m;3)各GNSS所使用坐标框架间转换参数引起的坐标变化达到厘米级。     

TRMM星载测雨雷达和地基雷达反射率因子数据的三维融合

TRMM卫星上的测雨雷达(TRMMPR)探测资料分布均匀且具有很高的垂直分辨率,但灵敏度较低;地基雷达(GR)水平分辨率较高且具有较高的灵敏度,但其垂直分辨率低。通过将TRMM PR与GR反射率因子数据的三维数据融合,得到了更优的反射率因子图像。测雨雷达与地基雷达三维数据融合主要分为以下几步:测雨雷达与地基雷达数据预处理——如去杂波、衰减校正;测雨雷达与地基雷达时空匹配;选取和应用合适的三维图像融合算法;对融合后的图像进行效果评估。试验结果表明:融合后的图像不仅增大了信息量,更好地检测弱降水,还提高了空间三维(3D)分辨率,能更好地反映降水区域细节,且使得数据总体上具有更高的完整性和可靠性。此外,还将基于雷达资料估测的降水数据与地面雨量计数据进行对比,估计反射率因子数据融合在降水测量上的有效性。      

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.