Outlier separability analysis with a multiple alternative hypotheses test

Outlier separability analysis is a fundamental component of modern geodetic measurement analysis, positioning, navigation, and many other applications. The current theory of outlier separability is based on using two alternative hypotheses—an assumption that may not necessarily be valid. In this paper, the current theory of outlier separability is statistically analysed and then extended to the general case, where there are multiple alternative hypotheses. Taking into consideration the complexity of the critical region and the probability density function of the outlier test, the bounds of the associated statistical decision probabilities are then developed. With this theory, the probabilities of committing type I, II, and III errors can be controlled so that the probability of successful identification of an outlier can be guaranteed when performing data snooping. The theoretical findings are then demonstrated using a simulated GPS point positioning example. Detailed analysis shows that the larger the correlation coefficient, between the outlier statistics, the smaller the probability of committing a type II error and the greater the probability of committing a type III error. When the correlation coefficient is greater than 0.8, there is a far greater chance of committing a type III error than committing a type II error. In addition, to guarantee successful identification of an outlier with a set probability, the minimal detectable size of the outlier (often called the Minimal Detectable Bias or MDB) should dramatically increase with the correlation coefficient.     

基于层次任务网络规划的分布式协同地理建模任务分解方法研究

为了有效地实现分布式协同地理建模中的合作规划和资源共享,通过分析分布式协同地理建模中任务分解过程,探讨地理建模任务的功能结构、计算复杂性、组织多方性和时空尺度等分解依据,采用层次任务网络(HTN)规划形式化表达地理建模任务,设计了基于顺序任务分解(OTD)的地理建模任务的递归分解算法,模拟领域专家寻求合作规划地理建模任务的思考方式。以分布式流域水文模型SWAT作为试验案例,开发并实现了地理建模任务HTN规划器,为分布式协同地理建模环境中的任务分解方法提供了新的思路。     

DMSP-OLS与NPP-VIIRS夜间灯光遥感影像数据整合

DMSP-OLS夜间灯光遥感数据截至2013年,现已被NPP-VIIRS夜间灯光数据取代。因此,要获得长时间序列且稳定的夜间灯光数据集,需要整合两类夜间灯光数据。基于此,本文提出了基于重采样的两类数据整合方法,对2013—2020年NPP-VIIRS数据进行模拟,最终建立了1992—2020年长时间序列校正—模拟DMSP-OLS夜光遥感数据集。结果表明,基于重采样的整合方法效果良好(城市区域Pearson相关系数ρ=0.9852,RMSE=3.4607),整合数据集与相关社会经济参考量高度契合(影像DN值总和与GDP的相关系数ρ=0.946,与人口的相关系数ρ=0.971,二次多项式模型拟合R²≈0.98,RMSE<5.55),优于已有研究。因此,利用该方法整合后的数据集能更好地支撑基于夜间灯光影像的长时间序列研究。     

A Composite Semisupervised SVM for Classification of Hyperspectral Images

This letter presents a novel composite semisupervised support vector machine (SVM) for the spectral-spatial classification of hyperspectral images. In particular, the proposed technique exploits the following: 1) unlabeled data for increasing the reliability of the training phase when few training samples are available and 2) composite kernel functions for simultaneously taking into account spectral and spatial information included in the considered image. Experiments carried out on a hyperspectral image pointed out the effectiveness of the presented technique, which resulted in a significant increase of the classification accuracy with respect to both supervised SVMs and progressive semisupervised SVMs with single kernels, as well as supervised SVMs with composite kernels.     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.     

The determination of potential difference by the joint application of measured and synthetical gravity data: a case study in Hungary

In an elementary approach every geometrical height difference between the staff points of a levelling line should have a corresponding average g value for the determination of potential difference in the Earth’s gravity field. In practice this condition requires as many gravity data as the number of staff points if linear variation of g is assumed between them. Because of the expensive fieldwork, the necessary data should be supplied from different sources. This study proposes an alternative solution, which is proved at a test bed located in the Mecsek Mountains, Southwest Hungary, where a detailed gravity survey, as dense as the staff point density (~1 point/34 m), is available along a 4.3-km-long levelling line. In the first part of the paper the effect of point density of gravity data on the accuracy of potential difference is investigated. The average g value is simply derived from two neighbouring g measurements along the levelling line, which are incrementally decimated in the consecutive turns of processing. The results show that the error of the potential difference between the endpoints of the line exceeds 0.1 mm in terms of length unit if the sampling distance is greater than 2 km. Thereafter, a suitable method for the densification of the decimated g measurements is provided. It is based on forward gravity modelling utilising a high-resolution digital terrain model, the normal gravity and the complete Bouguer anomalies. The test shows that the error is only in the order of 10⁻³mm even if the sampling distance of g measurements is 4 km. As a component of the error sources of levelling, the ambiguity of the levelled height difference which is the Euclidean distance between the inclined equipotential surfaces is also investigated. Although its effect accumulated along the test line is almost zero, it reaches 0.15 mm in a 1-km-long intermediate section of the line.     

Characterization of diurnal cycles in ZTD from a decade of global GPS observations

The diurnal cycle of the tropospheric zenith total delay (ZTD) is one of the most obvious signals for the various physical processes relating to climate change on a short time scale. However, the observation of such ZTD oscillations on a global scale with traditional techniques (e.g. radiosondes) is restricted due to limitations in spatial and temporal resolution. Nowadays, the International GNSS Service (IGS) provides an important data source for investigating the diurnal and semidiurnal cycles of ZTD and related climatic signals. In this paper, 10 years of ZTD data from 1997 to 2007 with a 2-hour temporal resolution are derived from global positioning system (GPS) observations taken at 151 globally distributed IGS reference stations. These time series are used to investigate diurnal and semidiurnal oscillations. Significant diurnal and semidiurnal oscillations of ZTD are found for all GPS stations used in this study. The diurnal cycles (24 hours period) have amplitudes between 0.2 and 10.9 mm with an uncertainty of about 0.5 mm and the semidiurnal cycles (12 h period) have amplitudes between 0.1 and 4.3 mm with an uncertainty of about 0.2 mm. The larger amplitudes of the diurnal and semidiurnal ZTD cycles are observed in the low-latitude equatorial areas. The peak times of the diurnal cycles spread over the whole day, while the peak value of the semidiurnal cycles occurs typically about local noon. These GPS-derived diurnal and semidiurnal ZTD signals are similar with the surface pressure tides derived from surface synoptic pressure observations, indicating that atmospheric tides are the main driver of the diurnal and semidiurnal ZTD variations.     

Preprocessing of gravity gradients at the GOCE high-level processing facility

One of the products derived from the gravity field and steady-state ocean circulation explorer (GOCE) observations are the gravity gradients. These gravity gradients are provided in the gradiometer reference frame (GRF) and are calibrated in-flight using satellite shaking and star sensor data. To use these gravity gradients for application in Earth scienes and gravity field analysis, additional preprocessing needs to be done, including corrections for temporal gravity field signals to isolate the static gravity field part, screening for outliers, calibration by comparison with existing external gravity field information and error assessment. The temporal gravity gradient corrections consist of tidal and nontidal corrections. These are all generally below the gravity gradient error level, which is predicted to show a 1/f behaviour for low frequencies. In the outlier detection, the 1/f error is compensated for by subtracting a local median from the data, while the data error is assessed using the median absolute deviation. The local median acts as a high-pass filter and it is robust as is the median absolute deviation. Three different methods have been implemented for the calibration of the gravity gradients. All three methods use a high-pass filter to compensate for the 1/f gravity gradient error. The baseline method uses state-of-the-art global gravity field models and the most accurate results are obtained if star sensor misalignments are estimated along with the calibration parameters. A second calibration method uses GOCE GPS data to estimate a low-degree gravity field model as well as gravity gradient scale factors. Both methods allow to estimate gravity gradient scale factors down to the 10⁻³ level. The third calibration method uses high accurate terrestrial gravity data in selected regions to validate the gravity gradient scale factors, focussing on the measurement band. Gravity gradient scale factors may be estimated down to the 10⁻² level with this method.     

顾及多个因素的城市建成区用地规模预测研究——以郑州市为例

目前,城市建成区用地规模受社会、经济、城市环境和政策等多因素影响,传统统计方法已经难以准确预测城市用地规模,从多因素的角度研究城市用地是合理的。BP神经网络法和多元回归分析法都是顾及多个因素的统计方法。以郑州市1984—2005年相关统计数据为样本数据,用BP神经网络法、多元回归分析法、灰色系统GM(1,1)法和Logistic法建立预测模型,对2006年和2007年建成区规模进行模拟预测。预测结果表明,顾及多个因素的预测方法预测精度较高,其中BP神经网络法优于多元回归分析法。     

Phase center modeling for LEO GPS receiver antennas and its impact on precise orbit determination

Most satellites in a low-Earth orbit (LEO) with demanding requirements on precise orbit determination (POD) are equipped with on-board receivers to collect the observations from Global Navigation Satellite systems (GNSS), such as the Global Positioning System (GPS). Limiting factors for LEO POD are nowadays mainly encountered with the modeling of the carrier phase observations, where a precise knowledge of the phase center location of the GNSS antennas is a prerequisite for high-precision orbit analyses. Since 5 November 2006 (GPS week 1400), absolute instead of relative values for the phase center location of GNSS receiver and transmitter antennas are adopted in the processing standards of the International GNSS Service (IGS). The absolute phase center modeling is based on robot calibrations for a number of terrestrial receiver antennas, whereas compatible antenna models were subsequently derived for the remaining terrestrial receiver antennas by conversion (from relative corrections), and for the GNSS transmitter antennas by estimation. However, consistent receiver antenna models for space missions such as GRACE and TerraSAR-X, which are equipped with non-geodetic receiver antennas, are only available since a short time from robot calibrations. We use GPS data of the aforementioned LEOs of the year 2007 together with the absolute antenna modeling to assess the presently achieved accuracy from state-of-the-art reduced-dynamic LEO POD strategies for absolute and relative navigation. Near-field multipath and cross-talk with active GPS occultation antennas turn out to be important and significant sources for systematic carrier phase measurement errors that are encountered in the actual spacecraft environments. We assess different methodologies for the in-flight determination of empirical phase pattern corrections for LEO receiver antennas and discuss their impact on POD. By means of independent K-band measurements, we show that zero-difference GRACE orbits can be significantly improved from about 10 to 6 mm K-band standard deviation when taking empirical phase corrections into account, and assess the impact of the corrections on precise baseline estimates and further applications such as gravity field recovery from kinematic LEO positions.