基于MAPGIS的市政设施数据模型研究

本文主要介绍了市政基本设施的组成,提出了基于MAPGIS基本数据组织的市政设施数据模型,并讨论了市政设施数据模型的实现。从而,利用该数据组织模型可实现城市级的市政设施管理的综合集成与共享,提高市政设施管理和养护水平。     

改进的自适应遗传算法在遥感图像分割中的应用

为了自动确定遥感图像分割的最佳阈值,本文提出了一种改进的自适应遗传算法,并利用该算法对二维Otsu图像阈值分割函数进行了全局优化,提高了分割闻值的求解速度。该算法能够根据个体适应度大小和群体的分散程度自动调整遗传控制参数,从而能够在保持群体多样性的同时加快收敛速度,克服基本遗传算法的收敛性差、易早熟问题。实验结果表明,该算法具有良好的收敛速度和稳定性,达到较好的图像分割效果,大大缩短了计算时间。     

城市框架数据的更新模式及应用研究

分析了利用竣工测量数据、VRS技术及高分辨率影像更新城市框架数据三种模式的可行性,给出了适合青岛地区框架数据的更新方案.     

GPS在计算导弹脱靶量中的应用研究

将GPS技术应用于导弹靶试脱靶量测量,并对计算过程作了较为详细的分析和探讨。试验证明：利用GPS测定脱靶量,测量精度高,简单易行,可以在靶场逐步试验推广。     

制图综合中移位算法概述分析

本文首先对移位算法的发展史进行简要介绍,然后,在其基础上,根据各移位算法的思想特征,对各移位算法进行归纳,将其划分为七类,并对每一类算法的核心思想进行详细论述,最后,对各类移位算法进行评价,分析其优缺点,总结各类算法中的可借鉴思想,为更好地实现移位奠定基础。     

GIS支持下的煤层气目标区模糊综合评价模型

地理信息系统(GIS)以空间数据库技术为核心,将空间要素与统计数据有机地结合在一起,具有直观灵活、分析功能强大、制表制图方便等优点。根据中国煤层气(CBM)目标区的特点,建立了4种原则下用于不同情形的煤层气目标区评价模型:第1种模型为全面考虑各个因素原则,第2种模型为只考虑重点因素原则,第3种模型为着重考虑重点因素原则,第4种模型为既全面考虑又兼顾重点因素原则。根据这4种模型,在常用GIS软件MapGIS基础上进行了二次开发并实现模型,以华北聚气区煤层气为例进行综合评价,目标区评价结果与实际情况基本相符。     

Environmental Investigation and Evaluation of Land Subsidence in the Datong Coalfield Based on InSAR Technology

Heavy mining of Jurassic and Carboniferous horizontal coal seams in the Datong coalfield has seriously affected the local geological environment, which is mainly manifested by such geohazards as soil avalanches, landslides, mudflows, surface subsidence, surface cracks, surface solid waste accumulation and surface deformation. More seriously, coal mining causes groundwater to leak. Overpumping of groundwater has resulted in substantial land subsidence of the urban area in Datong City. Based on the previous geo-environmental investigation in the work area, the authors used radar remote sensing techniques such as InSAR （synthetic aperture radar interferometry） and D-InSAR （differential synthetic aperture radar interferometry）, supplemented by the optical remote sensing method, for geo-environmental investigation to ascertain the geo-environmental background of the Datong Jurassic and Carboniferous-Permian coalfield and evaluate the effects of the geohazards, thus providing a basis for the geo-environmental protection, geohazard control and prevention, land improvement and optimization of the human environment. In this study 8 cog-nominal ERS-1/2 SAR data frames during 1992 to 2003 were used, but the following processing was made： （1） the multitemporal SAR magnitude images were used to interpret the geological structure, vegetation, microgeomorphology and drainage system; （2） the multi-temporal InSAR coherent images were used to make a classification of surface features and evaluate the coherence change due to coal mining; and （3） the multi-temporal cog-nominal SAR images were used to complete D-InSAR processing to remove the information of differential deformation areas （sites）. In the end, a ten-year time series of differential interferograms were obtained using the multi-temporal cog-nominal SAR images. In the tests, 84 deformed areas （sites） were obtained, belonging to those in 1993-1996, 1996-1997, 1997-1998, 1998-2001, 1998-2002 and 2001-2003 respectively. Of the 84 areas, 44 are m     

GNSS three carrier ambiguity resolution using ionosphere-reduced virtual signals

This paper presents a general modeling strategy for ambiguity resolution (AR) and position estimation (PE) using three or more phase-based ranging signals from a global navigation satellite system (GNSS). The proposed strategy will identify three best “virtual” signals to allow for more reliable AR under certain observational conditions characterized by ionospheric and tropospheric delay variability, level of phase noise and orbit accuracy. The selected virtual signals suffer from minimal or relatively low ionospheric effects, and thus are known as ionosphere-reduced virtual signals. As a result, the ionospheric parameters in the geometry-based observational models can be eliminated for long baselines, typically those of length tens to hundreds of kilometres. The proposed modeling comprises three major steps. Step 1 is the geometry-free determination of the extra-widelane (EWL) formed between the two closest L-band carrier measurements, directly from the two corresponding code measurements. Step 2 forms the second EWL signal and resolves the integer ambiguity with a geometry-based estimator alone or together with the first EWL. This is followed by a procedure to correct for the first-order ionospheric delay using the two ambiguity-fixed widelane (WL) signals derived from the integer-fixed EWL signals. Step 3 finds an independent narrow-lane (NL) signal, which is used together with a refined WL to resolve NL ambiguity with geometry-based integer estimation and search algorithms. As a result, the above two AR processes performed with WL/NL and EWL/WL signals respectively, either in sequence or in parallel, can support real time kinematic (RTK) positioning over baselines of tens to hundreds of kilometres, thus enabling centimetre-to-decimentre positioning at the local, regional and even global scales in the future.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

On the multivariate total least-squares approach to empirical coordinate transformations. Three algorithms

The multivariate total least-squares (MTLS) approach aims at estimating a matrix of parameters, Ξ, from a linear model (Y−E _Y = (X−E _X ) · Ξ) that includes an observation matrix, Y, another observation matrix, X, and matrices of randomly distributed errors, E _Y and E _X . Two special cases of the MTLS approach include the standard multivariate least-squares approach where only the observation matrix, Y, is perturbed by random errors and, on the other hand, the data least-squares approach where only the coefficient matrix X is affected by random errors. In a previous contribution, the authors derived an iterative algorithm to solve the MTLS problem by using the nonlinear Euler–Lagrange conditions. In this contribution, new lemmas are developed to analyze the iterative algorithm, modify it, and compare it with a new ‘closed form’ solution that is based on the singular-value decomposition. For an application, the total least-squares approach is used to estimate the affine transformation parameters that convert cadastral data from the old to the new Israeli datum. Technical aspects of this approach, such as scaling the data and fixing the columns in the coefficient matrix are investigated. This case study illuminates the issue of “symmetry” in the treatment of two sets of coordinates for identical point fields, a topic that had already been emphasized by Teunissen (1989, Festschrift to Torben Krarup, Geodetic Institute Bull no. 58, Copenhagen, Denmark, pp 335–342). The differences between the standard least-squares and the TLS approach are analyzed in terms of the estimated variance component and a first-order approximation of the dispersion matrix of the estimated parameters.