基于ArcGIS二次开发的数据库检查技术实现

介绍了以ArcGIS为数据处理平台,利用VBA语言进行的二次开发,进行数据库检查的技术实现。可以进行批量生产,具有快捷、简单、实用的特点。     

航空激光扫描测量系统在国外工程中的应用

航空激光扫描测量系统因为其高精度、高数字化,高自动化、高数据容量而吸引广大测量工作者的注意。本文对航空激光扫描测量系统的概念、原理及产品进行了介绍,就其在国外工程中的应用方法、技术难点、精度及系统的优缺点进行了分析,并对航空激光扫描测量的误差来源进行了分析,对它的应用前景和应用领域进行了展望。     

基于无人机影像的城市规划指标计算

在采用传统测绘方式形成测绘成果后,基于该成果进行建筑密度、绿地率、建筑占地面积、绿地面积等现状规划指标计算时,存在时效性差、便捷度低等问题。本文提出了一种利用无人机影像进行现状规划指标计算的思路和方法,开展研究并初步设计了一套基于无人机影像的规划指标计算系统。对广州市番禺区洛溪岛区域进行试验航飞,并利用图像分割方法对城市要素进行分析提取后,进行各类规划指标计算。     

城镇地籍调查中不同坐标系成果转换的研究

不同坐标系下的成果转换不仅要进行平移、旋转,还要进行缩放。论文主要研究应用Cass成图软件对控制点成果进行方便、快捷、准确地转换,详细论述了用Cass成图软件通过图解法进行不同坐标系控制点成果转换的方法、步骤,以及对含有高程信息的CAD图形的处理;并介绍了该方法的实际应用。     

基于激光点云数据的古建筑BIM几何模型构建

针对古建筑BIM各种信息模型中几何模型构建的问题,提出应用三维激光点云数据和CloudWorx软件进行建模的方法。应用Cyclone软件对点云数据进行去噪、配准,按构件进行分割;应用CloudWorx软件进行模型的构建。研究古建筑的瓦片、柱体、梁、墙体、围栏等构件的建模方法,通过模型合并生成古建筑的整体几何模型。     

MODIS数据预处理方法

时间、大气、太阳辐射的改变,导致获取的相邻遥感影像的对比度及光亮度产生差异,为了更好地进行分析、评价,需对遥感数据进行预处理。以新疆天山公路及其周边为研究区域,基于MODIS数据,利用ENVI软件进行数据的预处理,包括数据的几何校正、图像拼接、图像裁剪等,为其后进行的积雪研究作数据准备。     

GIS空间数据质量检查自动化的实践

为保证GIS空间数据表达的准确度、精度,数据组织的完整性、规范性、统一性,数据应用的可行性等,必须对数据进行质量检查。对GIS空间数据误差来源、检查方法、检查流程进行探讨,并开发了数据质量检查模块,对实验数据进行了质量检查,实验结果表明数据质量检查方法可行。     

圆柱面拟合方法研究

在工业测量中,为了对建筑物的圆柱面进行检测和修复,需要精确拟合圆柱面并对拟合结果进行分析和表达。文中选择几何模型、坐标变换、遗传算法3种圆柱面拟合方法进行实验,选取初值依赖程度、拟合的残差、圆度3个参数进行比较分析,通过实测数据的实验表明,3种算法的复杂度虽然不同,但经过合理的预处理都可以得到满足工程需求的结果。     

TEQC与QCVIEW32在GPS数据预处理中的应用

TEQC是GPS数据预理中常用的工具,可对数据进行格式转换、编辑和质量检查;绘图工具QCVIEW32能对TEQC质量检查的绘图文件进行可视化处理。将TEQC与QCVIEW32相结合,对GPS观测数据进行了预处理。结果表明,采用该方法能有效地进行可视化分析和误差剔除,进一步减小电离层延迟、周跳、多路径等误差对数据的影响,预处理后的GPS观测数据质量较好。     

基于GIS的地质图矢量化技术研究

将传统纸质媒介保存的地质图利用扫描仪转换为栅格图像,只能满足数字化存储的基本要求。要满足在信息化条件下进行地质图建库更新、共享利用与公开发布的要求,必须对地质图进行矢量化。应用现代GIS技术,使用通用的GIS软件平台,通过子图块拼接、几何校正、跟踪矢量化、地理底图标准化、矢量图编辑等过程实现地质图矢量化,对开展各类地勘活动、地学研究具有重要意义。本文结合浙江省地质图矢量化的实践,对利用GIS技术进行地质图矢量化的技术方法进行了阐述。     

基于ArcGIS的标准分幅裁切新方法

数据裁切是数据生产过程中相当重要的一个环节,也是耗时较长的一道工序。本文介绍了一种基于ArcGIS的简单实用的数据裁切新方法,可以进行批量生产,具有快捷、简单、实用的特点。     

Optimum data weighting and error calibration for estimation of gravitational parameters

Summary A new approach has been developed for determining consistent satellite-tracking data weights in solutions for the satellite-only gravitational models. The method employs subset least-squares solutions of the satellite data contained within the complete solution and requires that the differences of the parameters of subset solutions and the complete solution to be in agreement with their error estimates by adjusting the data weights. GEM-T2 model was recently computed and adjusted through a direct application of this method. The estimated data weights are markedly smaller than the weights implied by the formal uncertainties of the measurements. Orbital arc tests as well as surface gravity comparisons show significant improvements for solutions when more realistic data weighting is achieved.     

Spatial models with spatially lagged dependent variables and incomplete data

The purpose of this paper is to suggest estimators for the parameters of spatial models containing a spatially lagged dependent variable, as well as spatially lagged independent variables, and an incomplete data set. The specifications allow for nonstationarity, and the disturbance process of the model is specified non-parametrically. We consider various scenarios concerning the pattern of missing data points. One estimator we suggest is based on a smaller but complete subset of the sample; another is based on a larger but incomplete subset of the sample. We give large sample results for both of these cases.     

Examining the utility of random forest and AISA Eagle hyperspectral image data to predict Pinus patula age in KwaZulu-Natal,South Africa

High-data dimensionality is a common problem in hyperspectral data processing. Consequently, remote sensing techniques that reduce the number of bands are considered essential tools for most hyperspectral applications. The aim of this study was to examine the utility of the random forest ensemble to select the optimal subset of hyperspectral bands to predict the age of Pinus patula stands. Airborne AISA Eagle hyperspectral image data were collected over the study area. The random forest ensemble was used to test whether the forward or backward variable selection methods could identify the optimal subset of bands. Results indicate that both the selection methods produced high-predictive accuracies (root mean square error = 3.097 years). However, the backward variable selection method utilized 206 bands for the final model, while the forward variable selection utilized only a small subset of non-redundant bands (n = 9) while preserving the highest model accuracy (R ² = 0.6).     

基于小波与遗传算法的特征提取与特征选择

高维遥感数据的分类与识别与传统的多光谱遥感分类技术具有明显的区别。本文提出了一种基于遗传算法和小波／小波包分析相结合的特征提取方法用于高维遥感数据降维与分类。该方法综合了遗传算法的全局优化和小波／小波包分析的多尺度、多分辨率的特点。首先,通过离散的小波变换(DWT)或小波包变换(WP)将高光谱信号变换到特征域进行光谱分解。由于DWT变换是一种线性变换,不同尺度的DWT系数可作为线性光谱特征。然后,对这些线性光谱特征利用遗传算法结合训练样本计算类内／类间距离搜索最优分类子集,其具体染色体编码取可能的特征号,适应度函数基于样本平均Jeffries-Matusita距离计算。所用的分类器采用最大似然分类器。试验结果表明该方法与常规特征提取算法如主成分变换(PCA)、判别分析特征提取(DAFE)、决策边界特征提取(DBFE)相比,能提高分类精度约1.1％-6.5％。     

轨迹分割与图层融合的车辆轨迹线构建道路地图方法

传统道路地图构建方法将轨迹点（线）同等对待提取道路数据,忽略车辆轨迹的空间差异性,制约其结果精度与应用范围。为此,本文根据轨迹速度将轨迹线集分割滤选为3个轨迹线子集,将轨迹方向与Delaunay三角网模型集成探测路网拓扑结构;顾及轨迹线子集的特征差异选取不同参数值和约束条件并分层提取道路几何、交通语义数据,分别构建3个道路图层;运用缓冲区方法并根据道路几何、语义特征将多个道路图层融合为单个完整道路地图。运用出租车轨迹数据进行试验分析,结果表明：该方法顾及轨迹分布差异性,能将道路几何、交通语义信息融合提取,更适于处理复杂道路结构下的轨迹线。     

Developing a Raster Profile for the Spatial Data Transfer Standard

The Spatial Data Transfer Standard (SDTS) was designed to transfer both vector and raster data sets. In the early development of the SDTS, the designers recognized that there was a need to transfer raster data in addition to the more challenging vector data. As a result, the SDTS includes a “raster module” that accommodates a variety of raster data structures and formats. A raster profile is being developed that will exercise a selected subset of SDTS capabilities in order to provide a simple-to-use transfer of complete raster data sets.     

一种基于XML数据模型的时空查询代数探讨

时空查询代数是一种抽象的时空查询语言,它利用时空抽象数据类型的一部分基本的谓词、操作、函数来表达时空查询.而在时空微观运算上采用的数据结构是本源XML数据库(Native XML Database,NXD)用来存储半结构化数据的XML数据结构,所以对XML查询代数-XQuery FS的数据类型和查询操作做了时空扩展,并...     

Support Vector Machine (SVM) Classification: Comparison of Linkage Techniques Using a Clustering-Based Method for Training Data Selection

Support vectors, which usually compose a subset of training sets, determine the decision function of support vector machine (SVM) classification. Selecting a subset including the support vectors through reducing a large training set is a challenge. This paper examines how different linkage techniques in a clustering-based reduction method affect classification accuracy for semiarid vegetation mapping. The investigated linkage techniques include single, complete, weighted pairgroup average, and unweighted pair-group average. Using a multiple-angle remote sensing data set, there is no loss of SVM accuracy when the original training set is reduced to 21%, 14%, 20%, and 20% for these four linkage techniques, respectively.