遥感图谱认知理论与计算

近年来,随着对地观测技术的迅猛发展,卫星遥感领域逐渐进入了大数据时代。针对当前遥感应用的需求和特点,开展与视觉认知相结合的高分辨率遥感认知理论与方法研究是可行且必要的。在此背景下,受地学信息图谱思想启发,本文对遥感认知领域的图谱问题进行了研究,系统地提出了遥感图谱认知理论与计算方法论,旨在规范高分辨率遥感信息提取流程,构建精细化、定量化、智能化、综合化相结合的遥感信息解译模型。整套方法体系由横向“自底向上的分层抽象”和纵向“自顶向下的知识迁移”2个方向上的认知计算组成,分别对应了“由谱聚图”、“图谱协同”和“认图知谱”3大图谱转化过程。论文对涉及的概念、基本思想、关键技术及难点问题进行了重点分析,强调综合利用大数据、逐步融入知识来实现不同层次的遥感认知,以期为数据源极大丰富条件下的遥感信息解译提供新的视角。     

基于层次匹配的面状要素几何匹配方法研究

空间数据增量信息提取是空间数据库增量更新的基础,而要素几何匹配则为空间数据增量信息提取的关键流程之一。针对面状要素几何匹配时所产生的单一匹配算子匹配准确度低,以及多算子加权匹配算法匹配效率低、冗余度高、结果不准确等情况,本文提出一种基于多层次匹配的面状要素几何匹配算法。该算法以反映面状要素位置、大小、形状特征的质心匹配算子、面积重叠度算子以及转向角函数匹配算子为基础,并按各算子复杂度进行层次匹配规则设计,以提高算法效率、保证匹配的准确性。本文通过2种方法进行对比实验,结果表明：层次匹配算法匹配完整度和准确度均能达到90%以上,可以很好地进行面状要素几何匹配;与加权匹配方法相比效率更高,代价更小,且所得匹配结果比加权匹配方法所得结果更加准确。     

基于自适应参数估计的多时相遥感图像变化检测

提出一种基于自适应参数估计实现多时相遥感图像的变化检测算法.首先应用分层Markov随机场模型建立多时相差分图像的统计模型;然后通过非监督的迭代自适应参数估计实现差分图像的分类,从而检测出变化的像元.在每次参数估计过程中,先将上次估计出的参数用于最大后验估计实现差分图像的分类,然后根据分类结果再对参数进行修正,并将修正后的参数用于下一次迭代分类,如此循环迭代,能够自适应地完成差分图像的参数估计与分类.     

Regularities in Spatial Information Processing: Implications for Modeling Destination Choice

This paper investigates the determinants of spatial knowledge and how our knowledge of space varies according to geographic location. By using data on U.S. city names recalled at 22 test locations, a multivariate model of the information surface specific to each test location is calibrated. This model links the probability of a city being recalled from memory to its distance from the test site, its population size, its location with respect to other cities, and whether or not it is a state capital. The paper then suggests that these recall data provide information on spatial knowledge surfaces from which large-scale spatial choices, such as migration destinations, are made. Results from the analysis lend further evidence to the idea that spatial knowledge is stored and processed hierarchically and that individuals underrepresent information in large clusters. Consequently, the results have important implications for modeling any spatial behavior based on individuals' spatial information surfaces. In particular, the results cast further doubt on the validity of traditional large-scale spatial choice frameworks and lend support to the competing destinations hypothesis.     

Clustering of temporal event processes

A temporal point process is a sequence of points, each representing the occurrence time of an event. Each temporal point process is related to the behavior of an entity. As a result, clustering of temporal point processes can help differentiate between entities, thereby revealing patterns of behaviors. This study proposes a hierarchical cluster method for clustering temporal point processes based on the discrete Fréchet (DF) distance. The DF cluster method is divided into four steps: (1) constructing a DF similarity matrix between temporal point processes; (2) constructing a complete linkage hierarchical tree based on the DF similarity matrix; (3) clustering the point processes with a threshold determined by locating the local maxima on the curve of the pseudo-F statistic (an index which measures the separability between clusters and the compactness in clusters); and (4) identifying inner patterns for each cluster formed by a series of dense intervals, each of which contains at least one event of all processes of the cluster. The contributions of the article are: (1) the proposed DF cluster method can cluster temporal point processes into different groups and (2) more importantly, it can identify the inner pattern of each cluster. Two synthetic data sets were created to illustrate the DF distance between temporal point process clusters (the first data set) and validate the proposed DF cluster method (the second data set), respectively. An experiment and a comparison with a method based on dynamic time warping show that DF cluster successfully identifies the preconfigured patterns in the second synthetic data set. The cluster method was then applied to a population migration history data set for the Northern Plains of the United States, revealing some interesting population migration patterns.     

基于层次任务网络规划的分布式协同地理建模任务分解方法研究

为了有效地实现分布式协同地理建模中的合作规划和资源共享,通过分析分布式协同地理建模中任务分解过程,探讨地理建模任务的功能结构、计算复杂性、组织多方性和时空尺度等分解依据,采用层次任务网络(HTN)规划形式化表达地理建模任务,设计了基于顺序任务分解(OTD)的地理建模任务的递归分解算法,模拟领域专家寻求合作规划地理建模任务的思考方式。以分布式流域水文模型SWAT作为试验案例,开发并实现了地理建模任务HTN规划器,为分布式协同地理建模环境中的任务分解方法提供了新的思路。     

A progressive line simplification algorithm

An application of line simplification considering spatial knowledge is described. A method for identifying potential conflict regions, in order to avoid the self-intersection of generalized, lines, is also discussed. Further-more, a new progressive line simplification algorithm is presented. From the view point of spatial cognition, a spatial hierarchical structure is proposed, and its application to construction of spatial knowledge related to a line is explained.     

应用改进欧拉算法解算磁性目标空间位置参数

指出目前磁性目标探测中常规欧拉方法存在的局限性,提出在欧拉窗口内视地磁场和其他目标干扰异常联合影响为线性变化,将构造指数作为动态变化参数代入欧拉方程一并求解,并利用多元线性回归方法解决了欧拉方程非线性问题.在质量控制方案中,根据构造指数和目标深度变化规律,给出离散欧拉解滤波措施;采用层次聚类分析方法对滤波后欧拉解进行分...     

基于遥感技术的海岸带土地利用、覆被信息提取方法研究

本文以地处莱州湾东海岸的莱州市为研究区域,以TM影像为主要信息源,在分析各类地物分类效果的基础上,结合区域特点和地物光谱特征,综合利用两期典型季相的遥感图像,并针对每一种土地利用类型确定最佳信息提取影像,提出了基于光谱、地形等多种知识的分层信息提取方法.结果表明该方法分层目标明确,分类精度和计算效率都有明显提高,且在数...     

导航数字地图的快速显示技术研究

针对车辆导航系统中地图显示问题,本文设计了"LOD层+多层四叉树+R树"三级索引结构,对现有的四叉树分块方式进行了改进,提出了基于多层四叉树空间分块和块内根据R树分包的数据组织方案,有效解决了覆盖多个网格的要素存放问题;提出了多层和多块的数据缓冲区结构,以及相应的数据后台预取和更新策略,提高了教据提取效率.试验结果表明...