A Study Of Theory And Methods On Quality Control For Geometric Data

As a decision-making system of spatial relation, the fundamental task of GIS is to analyze and process spatial data, derive and abstract spatial information. The quality of data influences the reliability of the analysis results and realization of application objective directly, furthermore, affect the development of GIS industry. So it has important practical significance to study the theory and method of data quality control in GIS. This dissertation mainly focuses on the theories and algorithms of geometry error compensation and spatial data interpolation in the process of spatial data acquisition and analysis. The main works and contributions are summarized as follows:1. Theory of functional model fitting for geometric error is discussed, various existing models are compared and analyzed. The fitting model considering a priori information is studied in order to control the influences of control points. A robust fitting with prior information is presented which can resist outliers of spatial datum.2. A collocation method is proposed to fit geometric error of spatial data in order to compensate the remained local random errors, since the functional model can only fit the systematic errors or trend errors. The method for covariance function fitting in collocation is discussed and influences of the uncertainty of the covariance function are analyzed. To resist the influences of outliers, robust fitting for the covariance function and robust collocation are presented.3. In collocation applications, the prior covariance matrices between signals and observations should be consistent, otherwise, the solution of collocation will be twist. The variance component estimation is introduced to adjust the disharmony between covariance matrices of observations and random signals. The collocation based on maximum likelihood estimation, MINQUE estimation and Helmert estimation of variance components are studied. To balance the covariance matrices of the signals and the observations, a new adaptive collocation estimator is also derived in which the corresponding adaptive factor is constructed by the ratio of the variance components. In addition, the influences of adaptive factor on collocation results are analyzed. 4. The inherent geometrical or physical constraints should be satisfied while in fitting of geometric errors of spatial datum, the collocation estimators, with stochastic signal constraints, trend parameter constraints, as well as the synthetic constraints of signals and trend parameters, are derived based on the functional fitting with constraints. Considering there are many conditions which will lead to excessive calculation workload, a new idea of two-step adjustment is proposed.5. BP neural network is introduced to fit the geometric errors of GIS, which can approach the systematic errors without using a fixed functional model or stochastic model. Since the neural network has the disadvantages of slow learning speed and easily arriving at local minimum, an improved neural network algorithm is put forward.6. The principle and algorithm of minimum curvature interpolation are discussed. By analyzing we find that the fitting method based on the minimum curvature does not need the comprehensive knowledge about the characteristics of system errors or random errors, either the function model, stochastic model or network structure. At first the research area is divided into grids and then unknown data is interpolated grid by grid, therefore the fitting method based on the minimum curvature has good characteristics for the local fitting. As the result, it is more suitable for error fitting in the small area.7. In order to control the influences of the outliers or abnormal variations of the interpolation data in the process of data generation, the methods of equivalent weight average and robust trend surface fitting are proposed. The influences of kernel function, nodes and smoothing factor on multiquadric fitting are analyzed. An adaptive node choosing method, which cannot only ensure the stability, but also improve the precision of fitting, is proposed based on the effect of every node to the curve fitting calculated by using the orthogonal least squares.