一、P20 一般性问题

CH20080001类别制图与误差建模的概念模型=A Con- ceptual Framework for Categorical Mapping and Error Modeling[英]/张景雄(武汉大学遥感信息工程学院)//测绘学报.-2007,36(3).-296～301尽管离散目标和连续场的误差建模已得到了发展,名义场却存在实质性的和多半悬而未决的概念问题。致力于为确定信息和不确定特性整合出一个概念框架。这个概念模型是基于判别空间而构建的;后者是由面状类别时空表象的特质或驱动过程定义的。这个模型通过加入特定类的平均结构(其可进行基于判别变量的回归分析)的方式,奠定类别制图一致性的基础,并且使基于尺度的误差建模变得更为简便易行。这种误差建模可以有效地仿     

基于分区的局域神经网络时空建模方法研究

区域数据表现为两种尺度的空间特性:反映全局特征的空间依赖性和反映局域特征的空间波动性.空间波动性表现为空间数据在局部地区的聚集或高低交错现象.在研究区域数据时空预测性建模时,从降低数据的空间波动和不平稳性对模型预测能力的影响角度出发,提出了一种基于分区的局域神经网络时空非线性建模的思路.分区过程由基于空间邻接关系的K-means聚类算法完成.不同的分区方案通过相关性、波动性、紧凑性等指标进行评价和优选.在确定最优分区方案的基础上,对各子区分别采用两层前馈网络进行建模,模型的输入不仅要考虑本区内单元的作用,而且要考虑相邻子区的边界效应.各神经网络模型的时空预测能力通过平均相均差和动态相似率等指标进行衡量.最后,通过对法国94个县每周流感报告病例的时空建模分析表明,与全局神经网络模型相比,基于分区的局域神经网络模型具有更好的预测能力.     

一种基于局部判别正切空间排列的高光谱遥感影像降维方法

提出一种基于局部判别正切空间排列(local discriminative tangent space alignment,LDTSA)的高光谱影像降维方法。LDTSA源于局部正切空间排列(LTSA)中的排列机制,在一个局域块内利用线性局部正切平面对类内样本的流形结构建模,同时还考虑到类间判别信息以最大化判别边界。利用多幅高光谱数据进行降维和分类试验。结果表明,LDTSA主要有三个优点:①在小样本问题上性能稳定;②在降维过程中保持类别间的判别信息;③有效挖掘数据集的几何流形结构。     

不确定线-面拓扑关系的描述与判别

不确定线状目标和面状目标之间拓扑关系的描述是空间信息处理过程中经常面对的问题。在对不确定线状目标和面状目标进行描述的基础上,对不确定线状目标与面状目标各组成部分之间的相交程度进行了定量表达,通过计算这些度量组成的空间向量与9-交集模型确定的空间关系向量之间的相关度,提出了一种不确定线状目标和面状目标之间拓扑关系的描述模型,通过定量的方法来对其空间拓扑关系进行判别。     

面向带洞面状对象间的拓扑关系描述模型

为研究带洞面状对象间的拓扑关系,提出了一种25IM(25交集模型)。以点集拓扑理论为基础,对带洞面状区域的内部、边界和外部进行定义。分析了9IM(9交集模型)在表达带洞面状对象间拓扑关系方面存在的问题,将带洞面状对象分为内部、外边界、内边界、外边界外部、内边界外部共5部分,提出了一种5×5的矩阵模型,即25IM。基于点集拓扑理论,定义了8条规则来排除不符合逻辑的拓扑关系。基于25IM,对8种基本拓扑关系:相离、相接、重叠、覆盖、包含、相等、被覆盖和被包含,进行细分描述。结果表明,本文提出的25IM能够更为详细地表达带洞面状对象间的拓扑关系。     

一种基于网格的空间聚类方法在区域划分中的应用

区域划分是依据人口和社会经济指标将行政统计单元或其他地理实体划分成若干个不同水平或类别的集合。由于大多数的人口和社会经济指标来源于面状数据-行政统计单元,常用的区域划分的空间聚类方法是基于面状数据的,本文通过分析现有面状数据的聚类算法特点和不足,进而提出一种新的算法,该方法提出将面状统计单元进行网格划分,引入基于网格密度聚类算法的思想,克服现有面状聚类的诸多缺点,打破行政区划的限制,更好地发现潜在信息。     

网络RTK对流层误差建模方法及特性研究

网络RTK(real-time kinematic)定位中区域观测误差与基准站距离相关,对流层延迟误差的内插计算和改正的常规模型也受距离限制,本文深入研究了基于高程的对流层延迟误差建模的方法,并对其特性进行了分析.首先对对流层延迟误差空间尺度特性分析,得出了对流层延迟仅与高程强烈相关的结论,在此基础上研究并选取基于高程...     

平面随机线元等概率密度误差模型边界包络线

线状实体误差模型包络线既是GIS位置不确定性研究的重要内容,又是GIS可视化研究的关键指标.为了充分利用计算机技术求解符合GIS精度要求的误差模型包络线,基于文献[1,2]中探讨过的等概率密度误差模型建模机理和数值算法,研究了平面随机线元等概率密度误差模型边界包络线的确定原理和计算方法,并通过实例辅以可视化分析,验证了原理的正确性和可操作性.     

DEM线性建模过程中的高程误差传播

在建立数字高程模型(DEM)时,基于规则格网数据和基于三角形网数据的线性建模是常用的方法。在建模过程中,结点的高程误差会随之传播。研究了DEM在基于规则格网数据和基于三角形网数据的线性建模过程中的高程误差传播问题,推导出了在结点高程误差相关情形下的高程误差传播公式,求出了在规则格网和不规则格网面上的平均高程传播误差。平均高程传播误差可以作为DEM线性插值过程中高程误差传播的度量指标。     

导航用海洋重力异常图的孔斯曲面重构方法

本文基于地球重力物理场连续的特性,将计算机构图中的孔斯（Coons）曲面建模引入到导航用海洋重力异常图的加密重构中,建立了不同边界曲线的双一次Coons曲面和双三次C1Coons曲面重力异常模型。通过对实验区数据计算分析,结果表明基于Coons曲面建立的重力异常模型精度均高于双线性内插模型,且边界条件数越多,模型精度越高;同时利用现有全球SRTM30plus海深数据,借鉴于间接内插的思想,以布格异常基于Coons曲面进行重力异常图的重构,与空间重力异常直接建模相比,精度整体提高了25%。     

Discriminant Models of Uncertainty in Nominal Fields

Despite developments in error modeling in discrete objects and continuous fields, there exist substantial and largely unsolved conceptual problems in the domain of nominal fields. This article explores a novel strategy for uncertainty characterization in spatial categorical information. The proposed strategy is based on discriminant space, which is defined with essential properties or driving processes underlying spatial class occurrences, leading to discriminant models of uncertainty in area classes. This strategy reinforces consistency in categorical mapping by imposing class-specific mean structures that can be regressed against discriminant variables, and facilitates scale-dependent error modeling that can effectively emulate the variation found between observers in terms of classes, boundary positions, numbers of polygons, and boundary network topology. Based on simulated data, comparisons with stochastic simulation based on indicator kriging confirmed the replicability of the discriminant models, which work by determining the mean area classes based on discriminant variables and projecting spatially correlated residuals in discriminant space to uncertainty in area classes.     

Discriminant models for uncertainty characterization in area class change categorization

Discriminant space defining area classes is an important conceptual construct for uncertainty characterization in area-class maps.Discriminant models were promoted as they can enhance consistency in area-class mapping and replicability in error modeling.As area classes are rarely completely separable in empirically realized discriminant space,where class inseparabil-ity becomes more complicated for change categorization,we seek to quantify uncertainty in area classes(and change classes)due to measurement errors and semantic discrepancy separately and hence assess their relative margins objectively.Experiments using real datasets were carried out,and a Bayesian method was used to obtain change maps.We found that there are large differences be-tween uncertainty statistics referring to data classes and information classes.Therefore,uncertainty characterization in change categorization should be based on discriminant modeling of measurement errors and semantic mismatch analysis,enabling quanti-fication of uncertainty due to partially random measurement errors,and systematic categorical discrepancies,respectively.     

A New Combined Assessment of Mixed Uncertainty in Spatial Models: Conceptualization and Implementation

Uncertainty quantification is not often performed in spatial modeling applications, especially when there is a mixture of probabilistic and non‐probabilistic uncertainties. Furthermore, the effect of positional uncertainty is often not assessed, despite its relevance to geographical applications. Although there has been much work in investigating the aforementioned types of uncertainty in isolation, combined approaches have not been much researched. This has resulted in a lack of tools for conducting mixed uncertainty analyses that include positional uncertainty. This research addresses the issue by first presenting a new, flexible, simulation‐oriented conceptualization of positional uncertainty in geographic objects called F‐Objects. F‐Objects accommodates various representations of uncertainty, while remaining conceptually simple. Second, a new Python‐based framework is introduced, termed Wiggly and capable of conducting mixed uncertainty propagation using fuzzy Monte Carlo simulation (FMCS). FMCS combines both traditional Monte Carlo with fuzzy analysis in a so‐called hybrid approach. F‐Objects is implemented within the Wiggly framework, resulting in a tool capable of considering any combination of: (1) probabilistic variables; (2) fuzzy variables; and (3) positional uncertainty of objects (probabilistic/fuzzy). Finally, a realistic GIS‐based groundwater contamination problem demonstrates how F‐Objects and Wiggly can be used to assess the effect of positional uncertainty.     

基于Monte Carlo方法的不确定性地理现象可视化

应用Monte Carlo方法伪随机数模拟表达随机过程现象的数学方法，并结合粒子系统模型提出了一种地理现象空间分布不确定性特征的动画可视化方法。通过栅格单元的随机运动，从视觉上表达现象分布在空间定位、属性特征上的不确定性、模糊性，同时由Monte Carlo方法控制随机过程中现象分布的统计规律。     

基于ε_m模型的线元位置不确定性度量指标

首先研究了线元不确定性的εm模型,将该模型误差带边界线分为左边界线、右边界线、左误差半圆和右误差半圆四部分,利用代数的方法推导了这四部分误差带边界线的解析表达式;利用误差带边界线的解析表达式,绘出不确定性区域的图形,给出了平均误差带宽和误差带的面积作为线元不确定性的精度评估指标。     

Relationship of uncertainty between polygon segment and line segment for spatial data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of ?σ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of ?m and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.     

Relationship of Uncertainty Between Polygon Segment and Line Segment for Spatial Data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error band model of εp is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator, The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.     

A Distributed Geoprocessing Concept for Enhancing Terrain Analysis for Environmental Modeling

Geographic Information Systems (GIS) are well suited to support environmental modeling for dealing with space. However, some of the limitations of current GIS are the lack of tools for comprehensive documentation of the models, the inadequate representation of fields, and the deficient methodology for comprehensive management of uncertainty. Using Digital Terrain Modeling and Analysis as an example, this paper proposes an enhanced approach to overcome these drawbacks. It suggests implementing sophisticated functionality for modeling and analysis of fields in special-purpose modules outside monolithic GIS. These modules include three components: (1) an extensive framework for metainformation that allows a sound assessment of the fitness-for-use of digital field representations for environmental modeling applications, (2) an explicit digital representation of the field phenomenon equipped with the appropriate tools for the derivation of data, and (3) the methods to assess the quality of derived data. A standardized interface enables communication between the module and other software components. The presented modular approach combines the functionality of common GIS with highly specialized modeling and analysis tools encapsulating expert knowledge about the represented phenomena.