正倒垂法变形监测的原理与数据分析

大坝变形监测是一项精度非常高的工作,正倒垂作为一种十分常用的监测方法,在大坝的变形监测中发挥着巨大的作用。首先介绍了正倒垂法变形监测的原理与方法,通过采集隔河岩大坝四个测点的正倒垂观测数据,重点探讨和分析了大坝水平位移与环境量之间的关系及其变化规律,并由此得出一些相关结论,最后提出了正倒垂的一些改进措施。     

基于Auto CAD扩展属性的多边形面积统计汇总应用

提出在不规则多边形面积统计汇总工作中应用基于Auto CAD扩展属性的技术,对多边形的对象属性进行存储,将多边形图形数据与属性数据一体化结合,实现了多边形图形多元化处理,如统计分析、汇总报表、图形标注等的自动化、智能化,为不规则多边形面积处理提出了一种较新的处理思路。本文结合工程实例,通过对比分析详细介绍了该技术的具体应用过程。     

面向交通土建工程的网络化移动勘测办公系统

现今的交通土建类大型工程项目数据繁冗复杂。传统的数据处理方式速度慢、精度低,已不能满足大型工程管理的需要。GeoNMOS软件可弥补在大型工程项目中数据处理方面的不足。文中提出网络化移动勘测办公系统的设计理念,着重分析GeoNMOS各个模块的特点及其主要功能。     

顾及用户体验的三维城市模型自适应组织方法

传统基于“图层付象”的组织方法,没有考虑三维城市模型的不同内容以及不同细节层次的粒度差异,导致在网络环境下的传输效率低,难以满足多用户并发访问的流畅可视化。深入分析了大范围漫游与小范围聚焦的用户体验特点,通过元数据统筹管理和对象离散化,即时响应用户请求,减少无效数据传输,保证了多用户并发环境下的高效调度和浏览。针对模型LoI)和分解的对象,设计了结构统一的对象ID,隐式存储关联关系并支持分布式模型存储管理。以分布式数据库MongoDB为平台进行实验,验证了本文方法的可行性和有效性。     

一种空间域矢量地图数据盲水印算法

对目前已有的针对二维矢量数据的水印算法进行了分析,并提出了一种新的鲁棒的盲水印算法。该算法首先将矢量数据分块,然后在不同分块中,结合密钥修改某些点的x坐标或者y坐标,从而嵌入水印信息。实验证明,该算法对平移、坐标转换、制图综合、插值等常规操作以及地物对象顺序置乱等攻击具有较好的鲁棒性,并且是一种盲检算法,具有较好的实用价值。     

S形分布的数据拟合数学模型研究

S形曲线有着广泛的应用,以往的研究模型仅含三个参数,大多以对称S形分布的基本光滑数据为例,这使得自由度受限的和以理想数据为研究对象所得到的研究结果难以适用于更实际的、非对称(左偏S形分布、右偏S形分布)的非光滑观测数据。为此,提出并建立了含有5个参数的二次有理模型,作为对S形分布的观测数据进行拟合的模型,并进一步分为带导数拟合、5分段和值拟合和5特征点拟合,使S形分布的各种情况(左偏、对称和右偏)的拟合得到明显改善。     

基于主成分分析的ENVISAT ASAR数据在福建省漳州市香蕉园地提取中的应用(英文)

Banana is one of the main economic agrotypes in Zhangzhou, Fujian Province. The multi-temporal ENVISAT ASAR data with different polarization are used to classify the banana fields in this paper. Principal component analysis (PCA) was applied for six pairs of ASAR dual-polarization data. For its large leaves, banana has high backscatter. So the value of banana fields is high and shows very bright in the 1st component, which makes it much easier for banana fields extraction. Dual-polarization data provide more information, and the VV and VH backscatter of banana show different characters with other land covers. Based on the analysis of the radar signature of banana fields and other land covers and the 1st component, banana fields are classified using object-oriented classifier. Compared to the field survey data and ASTER data, the accuracy of banana fields in the study area is 83.5%. It shows that the principal component analysis provides the useful information in SAR images analysis and makes the extraction of banana fields easier. Supported by the Program for New Century Excellent Talents in University (NCET-05-0573), Fujian Science and Technology Project (No.2006I0018), the Science Project of the Education Department of Fujian Province(No. 2006F5022).     

基于Geodatabase的基础地理空间数据库设计

介绍了基于Geodatabase的空间数据牢设计方法,设计了城市基础地理空间数据库,并实现了城市基础地理空间数据的整合与共享。     

大型油罐沉降监测数据处理软件的实现

介绍了针对某批大型油罐的沉降监测数据处理软件制作过程,采用了Excel电子表格软件在VBA可视化编程环境下完成,实现了工程测量数据的及时处理分析。     

Exploring uncertainty in remotely sensed data with parallel coordinate plots

The existence of uncertainty in classified remotely sensed data necessitates the application of enhanced techniques for identifying and visualizing the various degrees of uncertainty. This paper, therefore, applies the multidimensional graphical data analysis technique of parallel coordinate plots (PCP) to visualize the uncertainty in Landsat Thematic Mapper (TM) data classified by the Maximum Likelihood Classifier (MLC) and Fuzzy C-Means (FCM). The Landsat TM data are from the Yellow River Delta, Shandong Province, China. Image classification with MLC and FCM provides the probability vector and fuzzy membership vector of each pixel. Based on these vectors, the Shannon's entropy (S.E.) of each pixel is calculated. PCPs are then produced for each classification output. The PCP axes denote the posterior probability vector and fuzzy membership vector and two additional axes represent S.E. and the associated degree of uncertainty. The PCPs highlight the distribution of probability values of different land cover types for each pixel, and also reflect the status of pixels with different degrees of uncertainty. Brushing functionality is then added to PCP visualization in order to highlight selected pixels of interest. This not only reduces the visualization uncertainty, but also provides invaluable information on the positional and spectral characteristics of targeted pixels.