欧氏聚类算法支持下的点云数据分割

欧氏聚类算法是多元统计中的一种重要分类方法,可以将其应用于测绘领域中点云数据的分割。本文首先计算点云数据中两点之间的欧氏距离,将距离小于指定阈值作为分为一类的判定准则;然后迭代计算,直至所有的类间距大于指定阈值,完成欧氏聚类分割。具体步骤为：①利用Octree法建立点云数据拓扑组织结构;②对每个点进行k近邻搜索,计算该点与k个邻近点之间的欧氏距离,最小归为一类;③设置一定的阈值,对步骤②迭代计算,直至所有类与类之间的距离大于指定阈值。试验证明,欧氏聚类算法对不同测量技术手段获取的点云数据均具有适用性,可以成功对点云数据进行分割,分割效果良好。     

时序分析在危岩体监测数据处理中的应用

引入时间序列分析进行危岩体监测数据的处理,建立了危岩体变形的动态模型,取得了较好的拟会与预报精度。     

大比例尺全野外数字化测图

全野外数字化测图的基本思想是将野外采集的各种有关地物和地貌信息转化为数字形式,通过数据接口传输给计算机进行处理,得到内容丰富的电子地图,需要时由电子计算机的图形输出设备(如显示器、绘图仪)绘出地形图或各种专题地图。     

Confidence Evaluation of Medium-and Long-Term Earthquake Prediction by Use of the Analytical Hierarchy Process

This paper offers an approach to the relative confidence level of medium- and long-term earthquake prediction methods by use of the analytical hierarchy process (AHP). We construct the criterion judgment matrices according to the results of an expert questionnaire known as the Delphi scheme. From the matrices we can obtain a scale for the relative preference or priority weight of each factor. The quantitative weights might be applied to make synthetic prediction of earthquake risk areas in the northern part of the North China seismic region.     

辽宁省“十五”数字化地磁数据分析系统

依据台站“十五”数字化地磁工作需求,结合地磁学科组运行管理的相关技术要求,研发辽宁省“十五”数字化地磁数据分析系统.该软件采用IDL作为开发语言,Oracle 10g数据库管理数据,主要包含用户管理模块、日志管理模块和数据管理模块3部分,具备数据实时处理、快速成像、操作简便、安全稳定等特点,可以有效解决目前台站地磁数据...     

地质资料存储方式的转变--浅谈地质资料光盘存储工作

本文分析了地质资料管理的现状;指出地质资料光盘存储是实现地质资料现代化管理的第一步,重点介绍了地质资料光盘存储工作的内容和过程,以及作者在工作实践中的几点体会;指出了地质资料光盘存储工作是实现地质资料存储方式转变,实现地宣现代化的重要基础。     

基于轨迹偏移算法的居民就医时空特征与空间格局分析

居民就医时空特征与空间格局反映了医疗设施的服务能力与布局合理性.本文以厦门岛为例,采用出租车轨迹数据,探讨了居民就医的时空特征和空间格局.论文提出了基于道路中心线的研究单元划分方法;提出OD轨迹偏移算法,更精细地提取出三级医院的就医OD数据,改善传统的缓冲区分析法中精确度较低的问题;对居民就医行为进行时空特征分析;基于...     

国产磁通门磁力仪稳定性分析

国产磁通门磁力仪(GM-3磁通门磁力仪)在全国各地磁台站推广应用,两年来,该仪器在武汉地震台工作比较稳定。本文对该仪器的基线值进行了多层线性回归分析。结果表明仪器性能比较稳定。     

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.