瞬时属性估计器在探地雷达数据处理中的应用

为了提高浅层埋藏目标识别的瞬时物理属性的精度,根据极平坦频率特性有限脉冲相应滤波器的思想,首次提出用它作为微分器和延时器组成解析信号瞬时属性估计器（FIFM）,对探地雷达信号的瞬时属性进行提取。结果表明,瞬时属性剖面的分辨率得到了明显的提高;采用瞬时属性估计器进行探地雷达信号瞬时参数的研究是一种非常有效的方法,可用于公路、桥梁、隧道等结构物的无损检测数据分析。     

一种融合光谱匹配和张量分析的高分辨率遥感影像目标探测器

提出了一种基于光谱分析理论和张量代数理论的高分辨率遥感影像目标探测器。首先在向量空间中对目标光谱进行特征匹配;然后对光谱特征与目标相似的像素进行空间\\|光谱特征一体化张量描述,进而在张量特征空间中对目标和背景进行分类。实验表明,张量学习机能够有效地对高分辨率影像进行目标识别。加入光谱特征匹配后的目标探测器能够极大地降低目标探测时间,同时进一步提高目标探测精度。     

Ability to detect and locate gross errors on DEM matching algorithm

Abstract

Digital elevation model (DEM) matching techniques have been extended to DEM deformation detection by substituting a robust estimator for the least squares estimator, in which terrain changes are treated as gross errors. However, all existing methods only emphasise their deformation detecting ability, and neglect another important aspect: only when the gross error can be detected and located, can this system be useful. This paper employs the gross error judgement matrix as a tool to make an in-depth analysis of this problem. The theoretical analyses and experimental results show that observations in the DEM matching algorithm in real applications have the ability to detect and locate gross errors. Therefore, treating the terrain changes as gross errors is theoretically feasible, allowing real DEM deformations to be detected by employing a surface matching technique.     

An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data

Abstract

Representations historically used within GIS assume a world that exists only in the present. Information contained within a spatial database may be added-to or modified over time, but a sense of change or dynamics through time is not maintained. This limitation of current GIS capabilities has recently received substantial attention, given the increasingly urgent need to better understand geographical processes and the cause-and-effect interrelationships between human activities and the environment. Models proposed so-far for the representation of spatiotemporal data are extensions of traditional raster and vector representations that can be seen as location- or feature-based, respectively, and are therefore best organized for performing either location-based or feature-based queries. Neither form is as well-suited for analysing overall temporal relationships of events and patterns of events throughout a geographical area as a temporally-based representation.

In the current paper, a new spatio-temporal data model is proposed that is based on time as its organizational basis, and is thereby intended to facilitate analysis of temporal relationships and patterns of change through time. This model is named the Event-based Spatio Temporal Data Model (ESTDM). It is shown that temporally-based queries relating to locations can be implemented in an efficient and conceptually straightforward manner using ESTDM by describing algorithms for three fundamental temporally-based retrieval tasks based on this model: (1) retrieving location(s) that changed to a given value at a given time, (2) retrieving location(s) that changed to a given value over a given temporal interval, and (3) calculation of the total area that has changed to a given value over a given temporal interval. An empirical comparison of the space efficiency of ESTDM and compressed and uncompressed forms of the ‘snapshot’ model is also given, showing that ESTDM is also a compact representation of spatio-temporal information.     

Statistical corrections of spatially interpolated missing precipitation data estimates

Spatial interpolation methods used for estimation of missing precipitation data generally under and overestimate the high and low extremes, respectively. This is a major limitation that plagues all spatial interpolation methods as observations from different sites are used in local or global variants of these methods for estimation of missing data. This study proposes bias‐correction methods similar to those used in climate change studies for correcting missing precipitation estimates provided by an optimal spatial interpolation method. The methods are applied to post‐interpolation estimates using quantile mapping, a variant of equi‐distant quantile matching and a new optimal single best estimator (SBE) scheme. The SBE is developed using a mixed‐integer nonlinear programming formulation. K‐fold cross validation of estimation and correction methods is carried out using 15 rain gauges in a temperate climatic region of the U.S. Exhaustive evaluation of bias‐corrected estimates is carried out using several statistical, error, performance and skill score measures. The differences among the bias‐correction methods, the effectiveness of the methods and their limitations are examined. The bias‐correction method based on a variant of equi‐distant quantile matching is recommended. Post‐interpolation bias corrections have preserved the site‐specific summary statistics with minor changes in the magnitudes of error and performance measures. The changes were found to be statistically insignificant based on parametric and nonparametric hypothesis tests. The correction methods provided improved skill scores with minimal changes in magnitudes of several extreme precipitation indices. The bias corrections of estimated data also brought site‐specific serial autocorrelations at different lags and transition states (dry‐to‐dry, dry‐to‐wet, wet‐to‐wet and wet‐to‐dry) close to those from the observed series. Bias corrections of missing data estimates provide better serially complete precipitation time series useful for climate change and variability studies in comparison to uncorrected filled data series. Copyright © 2013 John Wiley & Sons, Ltd.     

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The Modified Best Quadratic Unbiased Non-Negative Estimator (MBQUNE) of variance components

Estimated variance components may come out as negative numbers without physical meaning. One way out of this problem is to use non-negative methods. Different approaches have been presented for the solution. Sjöberg presented a method of Best Quadratic Unbiased Non-Negative Estimator (BQUNE) in the Gauss-Helmert model. This estimator does not exist in the general case. Here we present the Modified BQUNE (MBQUNE) obtained by a simple transformation from the misclosures used in the BQUE to residuals. In the Gauss-Markov adjustment model the BQUNE and MBQUNE are identical, and they differ in condition and Gauss-Helmert models only by a simple transformation. If the observations are composed of independent/disjunctive groups the MBQUNE exists in any adjustment model and it carries all the properties of the BQUNE (when it exists). The presented variance component models are tested numerically in some simple examples. It is shown that the MBQUNE works well for disjunctive groups of observations.     

双k型岭估计及其在GPS快速定位中的应用

针对GPS快速定位中设计阵病态性的特点,文中对现有的有偏估计进行了改进,提出了一种新的有偏估计--双k型岭估计.由广义岭估计和普通岭估计出发,讨论并给出了双k型岭估计中两个岭参数的选择方法.最后给出了实测GPS动态定位算例,验证了新估计的稳定性和有效性.     

时域漂移的泛克里格模型

将地质统计学中的泛克里格模型的漂移在时间域上进行计算, 在满足无偏性估计要求的条件下, 得到泛克里格模型的一种新形式。与原模型比较, 所得模型能够体现数据的时空特性, 并且模型形式得到简化。该模型适用于具有长期观测资料, 同时观测位置相对固定的估计和预测问题。     

非线性半参数模型在GPS系统误差处理中的应用

本文总结了当前测量界对系统误差的处理方法,提出了用非线性半参数核估计来处理GPS双差定位中系统误差的方法,即用非线性半参数模型中的非参数分量表达双差模型中残余的系统误差。最后通过算例检验了此方法对于提取GPS双差定位中残余的系统误差的有效性。