模拟退火算法在控制网平差中的应用

线性最小二乘估计在对非线性函数进行线性近似的过程中会产生模型误差,而一些非线性参数估计方法可能因为函数复杂而难以求导,法方程系数矩阵秩亏或呈病态矩阵时难以求解,非线性迭代解法有时对初始值的选择存在依赖性,不恰当的初始值会导致迭代无法收敛。针对这些问题,引入了模拟退火算法,介绍了该算法的基本原理、计算步骤和收敛性,并以3个控制网平差应用为例,说明该算法具有无需求导求逆,简洁实用,易于编程等优势,并能实现全局优化,获得高精度的平差结果。     

Using Topological Relationships to Inform a Data Integration Process

When spatial datasets are overlaid, corresponding features do not always coincide. This may be a result of the datasets having differing quality characteristics, being captured at different scales or perhaps being in different projections or datums. Data integration methods have been developed to bring such datasets into alignment. Although these methods attempt to maintain topological relationships within each dataset, spatial relationships between features in different datasets are generally not considered. The preservation of inter‐dataset topology is a research area of considerable current interest. This research addresses the preservation of topology within a data integration process. It describes the functional models established to represent a number of spatial relationships as observation equations. These are used to provide additional information concerning the relative positions of features. Since many topological relationships are best modelled as inequalities, an algorithm is developed to accommodate such relationships. The method, based on least squares with inequalities (LSI), is tested on simulated and real datasets. Results are presented to illustrate the optimal positioning solutions determined using all of the available information. In addition, updated quality parameters are provided at the level of the individual coordinate, enabling communication of local variation in the resultant quality of the integrated datasets.     

抗差最小二乘配置在数字化地图几何纠正中的应用讨论

抗差估计具有较好的抗拒异常观测值及粗差的能力,而最小二乘配置又能较好地处理系统误差,本文结合两者的优点,利用抗差最小二乘配置对数字化地图进行几何纠正,其中对协方差函数采用抗差拟合,得到了较好的结果。实验证明在GIS数据处理的扫描数字化地图几何纠正中,抗差最小二乘配置在抗拒异常值和处理系统误差方面优于单纯的最小二乘估计和单纯的最小二乘配置方法。     

动态最小二乘支持向量机学习算法

针对大样本集的训练问题和动态训练样本的模型更新问题,提出了动态最小二乘支持向量机学习算法.该算法充分利用已建好的模型,逐渐加入新样本,并可删除位于任何位置的非支持向量,避免了矩阵求逆运算,保证了算法的高效率.大坝变形及电离层延迟两个时间序列的预报实例表明,该算法具有计算时间短、预报精度高的特点.     

中国数字地质调查系统的基本构架 及其核心技术的实现

数字地质调查系统是贯穿整个地质矿产资源调查全过程的软件,涵盖地质矿产调查、矿产资源勘查、矿体模拟、品住估计、资源量估算、矿山开采系统优化等内容。考虑数字地质调查系统的应用技术层面,从数据“层”模型、数据流“池”技术、不同阶段数据模型继承技术、数据互操作技术等几个方面讨论了核心技术及其实现,通过基于无缝一体化技术的数据采集、管理、综合处理与成果表达,在实体或矿块的矿床建模技术与品住估计、储量估算等方面展现了全地质调查过程的数字化,不但为地质人员应用高新技术降低了门坎,而且极大地提高了研究精度和效率,丰富了成果的表现形式和服务形式。随着数字地质调查系统的完善和应用水平的提高,数字地质调查系统将成为中国地质调查的主流软件体系。     

Comparison of structural and least-squares lines for estimating geologic relations

Two different goals in fitting straight lines to data are to estimate a true linear relation (physical law) and to predict values of the dependent variable with the smallest possible error. Regarding the first goal, a Monte Carlo study indicated that the structural-analysis (SA) method of fitting straight lines to data is superior to the ordinary least-squares (OLS) method for estimating true straight-line relations. Number of data points, slope and intercept of the true relation, and variances of the errors associated with the independent (X) and dependent (Y) variables influence the degree of agreement. For example, differences between the two line-fitting methods decrease as error in X becomes small relative to error in Y. Regarding the second goal—predicting the dependent variable—OLS is better than SA. Again, the difference diminishes as X takes on less error relative to Y. With respect to estimation of slope and intercept and prediction of Y, agreement between Monte Carlo results and large-sample theory was very good for sample sizes of 100, and fair to good for sample sizes of 20. The procedures and error measures are illustrated with two geologic examples.     

基于卡尔曼滤波的时间序列影像背景估计

文章将卡尔曼滤波理论应用于时间序列影像的背景估计,引入衰减记忆因子解决滤波的发散问题,最终实现了对背景信号的估计。     

航天INSAR复影像对的自动快速匹配

针对航天INSAR主、辅影像数据的特点，提出了加大匹配窗口、加大锚点间隔的匹配策略，以提高匹配的可靠性和速度。在匹配过程中，综合利用轨道参数和强度影像金字塔进行粗匹配，并对强度影像采用相关系数与最小二乘相结合的双向匹配方案，实现了自动、快速、高可靠性和高精度的航天INSAR复影像匹配。最后总结了航天INSAR复影像匹配的特点。