Least-squares collocation with covariance-matching constraints

Most geostatistical methods for spatial random field (SRF) prediction using discrete data, including least-squares collocation (LSC) and the various forms of kriging, rely on the use of prior models describing the spatial correlation of the unknown field at hand over its domain. Based upon an optimal criterion of maximum local accuracy, LSC provides an unbiased field estimate that has the smallest mean squared prediction error, at every computation point, among any other linear prediction method that uses the same data. However, LSC field estimates do not reproduce the spatial variability which is implied by the adopted covariance (CV) functions of the corresponding unknown signals. This smoothing effect can be considered as a critical drawback in the sense that the spatio-statistical structure of the unknown SRF (e.g., the disturbing potential in the case of gravity field modeling) is not preserved during its optimal estimation process. If the objective for estimating a SRF from its observed functionals requires spatial variability to be represented in a pragmatic way then the results obtained through LSC may pose limitations for further inference and modeling in Earth-related physical processes, despite their local optimality in terms of minimum mean squared prediction error. The aim of this paper is to present an approach that enhances LSC-based field estimates by eliminating their inherent smoothing effect, while preserving most of their local prediction accuracy. Our methodology consists of correcting a posteriori the optimal result obtained from LSC in such a way that the new field estimate matches the spatial correlation structure implied by the signal CV function. Furthermore, an optimal criterion is imposed on the CV-matching field estimator that minimizes the loss in local prediction accuracy (in the mean squared sense) which occurs when we transform the LSC solution to fit the spatial correlation of the underlying SRF.     

图像相关匹配算法的快速实现

结合分级关键点互相关迭代法与搜索空间标记法,设计了一种有效提高图像相关匹配速度的算法。其中分级关键点互相关迭代算法快速实现了由重要到不重要像素点的逐级迭代互相关匹配计算,算法在保证精度的前提下可以随时结束并输出相关匹配值;而搜索空间标记法则能快速排除掉大量参数空间内不可能匹配的点。实验证明,这种算法能在正确配准图像的前提下大幅度提高匹配速度。     

IHS方法在QuickBird数据融合中存在的问题及其改进

 针对IHS变换在QuickBird数据融合中存在的光谱扭曲问题，提出了利用Visual-Pan波段和线性加权匹配两种方法进行改进 ，并给出了Visual-Pan波段方法中系数α的最佳取值范围，以及线性加权匹配融合图像的空间特征与光谱特征达到最佳效果时Pan 权值(wPan)和I权值(wI)的最佳取值。结果表明，对于Visual-Pan方法而言，当0.2＜α＜0.25时，可以获得非常好的融合效果; 而 对于线形加权匹配方法而言，当wpan=3/4、wI=1/4时，融合图像的空间特征与光谱特征可以达到最佳效果。     

GIS中地理边线不一致性的处理

地理边线的不一致性是GIS多源空间数据集成、建库和分析中的常见问题。首先分析了地理边线不一致性的类型,并归结为顶点数目完全和不完全匹配两种情况,其中前者可以直接利用结点广义捕捉算法处理。为了匹配后者的顶点数目,提出了投影配点的标准化处理,并导出了投影点的坐标计算和误差估计公式。此外,还导出了一致化改正后地理边线长度的误差估计公式。最后通过算例说明所提出方法的实现过程。     

GIS知识引导的正射影像变化检测及更新

针对数字正射影像变化检测问题的复杂性,提出了一种基于GIS知识引导的多维变化模板分析方法,探讨了由粗到细的四叉树变化检测模板生成方法和一些利用GIS知识的专家打分方法。利用此方法,对一个实验区不同时期的数字正射影像进行了变化检测和正射影像库的更新,达到了预期的目的。     

基于GIS的连锁超市物流配送信息系统的设计与实现

在市场竞争日益敏烈的形势下，各大连锁超市公司为了赢得客户，占领市场，陆续开展零售商品送货上门服务业务和电子商务业务。根据连锁超市送货上门服务的配送特点，本文简要介绍了基于GIS的连锁超市物流配送信息系统的设计框架。在此基础上，介绍了系统数据库的组织结构和构建方法，通过实例较为详细的论述了系统功能中的客户地址匹配、超市配送对象设计、配送路径设计的实现过程。     

数字地图在车辆导航中应用的若干技术问题

分析了对各种车辆导航系统对数字地图的使用需求，提出了车辆导航专用数字地图的特点和建立车辆导航专用数字地图库的必要性，在此基础上对数字地图在车辆导航中的若干技术问题作了一些探讨，并提出了一种在大范围内实现路径规划的技术途径。     

基于线要素的图幅自动接边问题的探讨

文中介绍了基于线要素的图幅自动接边的基本算法 ,主要分 3步完成 :待拼接数据链的提取 ,属性匹配和图形拼接 ,给出了具体的实现过程 ,并应用某区的地貌数据进行了实际检验。同时还进一步探讨了交通信息管理中图幅之间道路要素的拓扑连接问题     

A HYBRID THINNING ALGORITHM FOR BINARY TOPOGRAPHY MAP

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Surface Matching and Change Detection Using a Modified Hough Transformation for Robust Parameter Estimation

Many photogrammetric and GIS applications, such as city modelling, change detection and object recognition, deal with surfaces. Change detection involves looking for differences between two surface models that are obtained from different sensors, for example an optical sensor and a laser scanner, or by the same sensor at different epochs. Surfaces obtained through a sampling process may also have to be compared for future processing (for example transformation parameter estimation and change detection). Surface matching is therefore an essential task in these applications. The matching of surfaces involves two steps. The first step deals with finding the correspondences between two surface points and/or patches. The second step requires the determination of transformation parameters between the two surfaces. However, since most surfaces consist of randomly distributed discrete points and may have different reference systems, finding the correspondences cannot be achieved without knowing the transformation parameters between the two surfaces. Conversely, deriving the transformation parameters requires the knowledge of the correspondence between the two point sets. The suggested approach for surface matching deals with randomly distributed data sets without the need for error prone interpolation and requires no point-to-point correspondence between the two surfaces under consideration. This research simultaneously solves for the correspondence and the transformation parameters using a Modified Iterated Hough Transform for robust parameter estimation. Several experiments are conducted to prove the feasibility and the robustness of the suggested approach, even when a high percentage of change exists.