基于空间数据库的山东城镇发展水平的空间统计分析

基于GIS空间数据库,对山东省108个城镇的综合发展水平进行了空间统计分析。首先选用了反映城市综合发展水平的14项指标,并进行了主成分分析,得出反映城镇综合发展水平的综合指标,并分析了山东省各城镇的综合发展水平与分布状况。然后利用该综合指标,对各个城镇进行了空间统计分析,通过计算全局空间自相关指标,分析认为山东省各城镇综合发展水平总体上未呈现空间相关关系;通过计算局部空间自相关指标,分析认为山东省68.5%的城镇分布存在非相似空间集聚特征。     

全站仪交汇法在高速公路路面沉降监测中的应用

文章介绍了全站仪交会法沉降监测,方便简洁地解决了测量人员不能进入的高速公路等特殊位置的测量问题,并通过对该方法的精度分析,论证了其可行性和有效性,对以后类似的测量工作提供经验和依据。     

葫芦岛地区道路网络的建立

本文针对城市交通道路网的特点,根据现实的交通网络,对葫芦岛地区道路网络进行建立及分析。利用ArcGIS中的Geodatabase构建路网数据库,利用节点一弧段数据结构构建空间网络拓扑关系,生成点一弧矢量数据模型表示的道路网络。并实现四种网络分析功能:最佳路径分析、空间查询、追踪分析和缓冲区分析。     

单中继星单低轨卫星的联合定轨精度分析

针对由单中继星和单低轨卫星组成的联合定轨系统,给出了系统内不同轨道卫星摄动项的选取方案和卫星间的可见性判别模型。在模拟出含有白噪声的四程测距观测数据文件的基础上,研究了测距精度和采样弧段对联合定轨中高、低轨卫星定轨精度的影响。得出如下结论:联合定轨更有利于对低轨卫星的轨道改进;同样的采样时间条件下,测距噪声越小定轨精度越高,并且采样时间越短它的影响越明显;同样的测距噪声条件下,所用资料的采样时间越长精度越高,但当测距精度很高时,TDRS达到最好定轨精度所需的采样时间相应有所缩短。并在定出轨道后进行了轨道预报,分析了轨道预报的趋势及精度。     

全站仪似水准法和对向法高程测量的比较研究

通过对似水准法和对向法高程测量原理、误差来源及精度的比较分析,我们发现随着距离和竖直角度的增大,对向法高程测量中误差的变化大于似水准法高程测量中误差的变化;当两观测点间的水平距离小于等于1km时,对向法高程测量精度一般高于似水准法高程测量精度,但是当两观测点间的水平距离大于1km时,似水准法高程测量精度一般高于对向法高程测量精度。     

基于GIS的区域经济统计分析系统的设计与实现

随着经济的发展,区域间经济相互作用和影响的关系越来越密切,忽略空间相互作用的传统区域经济分析手段和方法开始显现出一些明显的不足。而GIS在处理和分析区域地理数据上具有强大的空间分析能力,为此我们引入GIS工具对区域经济进行分析,相较于传统的分析手法有了很大改善。本文以江西省区域经济数据为例,设计了一个基于GIS的区域经济统计分析系统,并利用Arcob-jects二次开发控件结合VB可视化编程语言,实现了大部分区域经济统计分析功能,最后并将结果进行网络发布。研究结果表明将GIS引入到区域经济统计分析中,更能正确、有效的揭示区域经济发展在空间上的联系,为区域经济研究决策提供服务。     

基于小波变换的单频GPS快速精密定位整周模糊度的解算

对于GPS短基线,载波相位双差观测量已基本消除了卫星轨道误差、钟差、大气折射误差等系统偏差的影响,主要包含距离观测量信息及随机测量误差,其中测量误差是高频的测量噪声,小波变换可将GPS载波相位双差观测量中的观测噪声(高频部分)分解出来。本文利用Coiflets小波基函数对GPS快速定位的原始载波相位双差观测量进行5层分解,通过重构第5层低频系数获得去除噪声的"干净"的载波相位双差观测量,然后利用"干净"的双差观测量进行最小二乘参数估计,以减小测量噪声对GPS快速定位病态方程解的扰动。计算结果表明该方法能够显著提高GPS快速定位中模糊度浮点解的精度,仅利用几个观测历元的数据就可以准确地固定模糊度。     

Approaches for comparative evaluation of raster GIS-based landslide susceptibility zonation maps

Evaluation of maps generated from different conceptual models or data processing approaches at spatial level has importance in many geoenvironmental applications. This paper addresses the spatial comparison of different landslide susceptibility zonation (LSZ) raster maps of the same area derived from various procedures.     

From 2D TO 3D Through Modelling Based On A Single Image

Three-dimensional modelling from single images remains an interesting topic of investigation in the research community, even though range sensors are becoming a common alternative for the generation of 3D information. The interest in single-image-based modelling is motivated by a wide spectrum of applications such as cultural heritage, civil engineering, urban planning and even criminology. In this paper a complete new production flowline is presented for modelling based on a single image. The modelling process consists of a series of familiar steps in photogrammetry and computer vision: feature extraction, vanishing point computation, camera self-calibration, 3D reconstruction and dimensional analysis. In particular, the methodology developed for single-image-based modelling takes a scientific approach combining several proven techniques with robust estimators. Finally, in order to demonstrate its capabilities, the reported examples include several real situations applied in different contexts.     

A framework of region-based spatial relations for non-overlapping features and its application in object based image analysis

Object based image analysis (OBIA) is an approach increasingly used in classifying high spatial resolution remote sensing images. Object based image classifiers first segment an image into objects (or image segments), and then classify these objects based on their attributes and spatial relations. Numerous algorithms exist for the first step of the OBIA process, i.e. image segmentation. However, less research has been conducted on the object classification part of OBIA, in particular the spatial relations between objects that are commonly used to construct rules for classifying image objects and refining classification results. In this paper, we establish a context where objects are areal (not points or lines) and non-overlapping (we call this “single-valued” space), and propose a framework of binary spatial relations between segmented objects to aid in object classification. In this framework, scale-dependent “line-like objects” and “point-like objects” are identified from areal objects based on their shapes. Generally, disjoint and meet are the only two possible topological relations between two non-overlapping areal objects. However, a number of quasi- topological relations can be defined when the shapes of the objects involved are considered. Some of these relations are fuzzy and thus quantitatively defined. In addition, we define the concepts of line-like objects (e.g. roads) and point-like objects (e.g. wells), and develop the relations between two line-like objects or two point-like objects. For completeness, cardinal direction relations and distance relations are also introduced in the proposed context. Finally, we implement the framework to extract roads and moving vehicles from an aerial photo. The promising results suggest that our methods can be a valuable tool in defining rules for object based image analysis.