基于空间曲面拟合的自适应阈值选取方法

提出了一种基于空间多项式曲面拟合的自适应阈值选取方法,该方法初步考虑了图像上空间目标、背景及像素的空间分布特征,拟合了图像及其空间分布结构。     

三峡库区水质数据时间序列分析预测研究

阐述了时间序列分析的概念、种类及分析方法,以三峡库区水质各指标的水期时间序列数据为研究对象,采用Holt-Winters时间序列预测模型进行了水质预测的试验研究.     

基于均值-标准差的K均值初始聚类中心选取算法

遥感图像分类是遥感图像处理中长期存在的一个难题，针对不同的传感器图像，不同的应用需求，选择合适的分类算法非常重要。在分类中不仅要考虑分类的精度，而且要考虑分类效率。本文研究了K均值算法的初始聚类中心的选择对算法本身聚类精度及效率的影响，提出了一种高效高精度的初始聚类中心选取方案，实验结果表明。利用该算法进行地表分类，效率比ENVI的K-Means（K均值）模块高。     

GIS基础地理数据质量检查及其监控体系构建

随着GIS应用的日益广泛,其基础地理数据质量问题越来越得到重视.本文对GIS基础地理数据的质量从检查内容、误差源、检查方法方面进行了研究,最后以数字线化图为例对软件监控方法的主要内容进行了探讨.     

多基线-数字近景摄影测量

根据交会精度与影像匹配自动化的需要,与当今航空数码相机沿航线方向视场角偏小的事实,阐述了大重叠、多基线摄影测量的必要性及其现实意义;重点介绍了利用非量测相机进行多基线的正直、交向摄影方法与数据处理的特点,特别是“多基线交向摄影”,通过多次、多相机对三峡工程地下电站尾水边坡进行拍摄试验,获得了传统非量测相机难以比拟的、非常满意的结果。     

基于区间树索引的等高线提取算法

重新设计了从高程格网中提取等高线过程中的遍历策略,以保证提取结果具有统一的方向;针对日益增长的高程格网数据量,提出了基于区间树索引来查找等高线起点的算法。     

鄱阳湖富营养化高光谱遥感监测模型初探

基于对光谱反射率与水质参数的相关分析,分别选取特征波长建立水质参数高光谱估测模型。结合修正营养状态指数,对湖泊的富营养化程度进行了监测和评价。结果表明:①总氮、总磷含量和透明度值的高光谱估测模型效果较理想;②单项指数评价水体富营养化水平其结果存在较大差异,综合考虑多个指标,计算营养指数的平均值,可以对富营养化程度进行正确的评价;③由于悬浮物浓度变化较大,掩盖了水体的叶绿素a信息,以致叶绿素a估测模型不具有通用性,为了完善叶绿素a浓度估测模型需要获得大范围、多季节的光谱数据,以便建立更有代表性和通用性的模型;④评价结果显示,鄱阳湖呈现轻度到中度富营养化状态,需要采取有力的保护措施防止进一步恶化。     

武汉市城市基础设施管理信息系统的开发与应用

阐述武汉市城市基础设施管理信息系统的研究与开发,包括系统的总体设计、数据的分析、技术架构、功能设计等。系统建设过程中首次在武汉市大规模对城市基础设施进行详细、准确的测量和调查,并建立竣工测量机制,使城市建设管理部门能够准确地掌握武汉市的城市基础设施状况,提高武汉市城市基础设施规划、建设、管理水平与效率。     

A high-precision heuristic model to detect home and work locations from smart card data

Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers. Processing and analyzing these data open new opportunities in urban modeling and travel behavior research. This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations. The model uses journey counts as an indicator of usage regularity, visit-frequency to identify activity locations for regular commuters, and stay-time for the classification of work and home locations and activities. London is taken as a case study, and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey. Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision. This study offers a new and cost-effective approach to travel behavior and demand research.     

Re-examining urban region and inferring regional function based on spatial–temporal interaction

Urban system is shaped by the interactions between different regions and regions planned by the government, then reshaped by human activities and residents’ needs. Understanding the changes of regional structure and dynamics of city function based on the residents’ movement demand are important to evaluate and adjust the planning and management of urban services and internal structures. This paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition, for purpose of understanding the higher order interactive population mobility and its impact on urban structure changes. First, a four-dimensional tensor of time (T)?×?week (W)?×?origin (O)?×?destination (D) was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend pattern. Then we reclassified the urban regions based on the space clustering formed by the space factor matrix and core tensor. Finally, we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each region. Our research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.