Research On Automated Matching Methods For Multi-Scale Vector Spatial Data Based On Global Consistency Evaluation

With the rapid improvement of acquisition technology of Geo-spatial information, the capabilities for acquisition of geo-spatial information have been greatly enhanced than before. A great deal of spatial data has been produced by various departments for different purposes and requirements of applications, resulting in the increasing data that represents the same region with different sources, different types, different phases, different precisions and different scales. So how to achieve effective management and comprehensive utilization of these spatial data are difficult issues that need to be urgently resolved in development of GIS, among these difficult issues, particularly incremental and propagative updating of spatial data, data integration and fusion. To automated recognize homologous features and build matching relationship between them from different versions of spatial data is the premise and crux of effective resolution of the above issues. Based on the above matching result, many applications, such as the discovery and extraction of change information, fusion of semantic and location information between homologous features, will be achieved. In fact, homologous feature matching has become an inevitable demand and bottleneck of numurous applications of geospatial data, which has very important theoretical and practice significance.Based on the idea of global consistency evaluation, the thesis selected the road networks and settlements that have typical features as the experimental subject to do research on multi-scale vector data matching. The main contribution of this paper is as follows:(1) A conceptual model of multi-scale vector spatial data matching based on global consistency evaluation is proposed. It consists of three critical components which are spatial structure characteristics recognition between features in datasets, synchronous search of potential matching candidates, global consistency evaluation, respectively.(2)It is proposed a hierarchical representation model of spatial structure characteristics of road networks. Aiming at the shortcomings of the traditional"Node-Arc"model, based on the principle of Gestalt"visual continuity"and consistency of properties, all road strokes have been extracted and characterized quantitatively the topological properties by social networks analysis model. It is also putted forward the construction of hierarchical road meshes based on analysis of road grade, thus being achieved hierarchical partition of morphology structure of road networks. Through describing adjacent and hierarchical relationships between road meshes, the hierarchical representation model of spatial structure characteristics of road networks based on road mesh has been established.(3)A matching strategy of synchronous search of potential matching candidates and global similarity evaluation is put forward. According to spatial structure relationship between features in data sources, neighborhood features of the selected feature to be matched are automated identified. The selected feature to be matched and its neighborhood features are treated as a whole to constitute a spatial scene to be matched. Potential matching candidates of all of these features in spatial scene are identified firstly, and then, incorrect potential matching candidates are excluded by making full advantage of the knowledge of spatial structure characteristics as a result the scale of potential matching candidates set decreases greatly. According to spatial structure characteristics of potential matching candidates of the selected feature to be matched, all of the objects in potential matching candidate sets will be combined to build several potential matching spatial scenes, laying an important foundation for global similarity evaluation of spatial scene. This method has been confirmed to be accuracy and effectiveness by doing experiment in road networks matching and settlement matching.(4) It is put forward a matching method for multi-scale vector road networks based on global consistency evaluation. Having selected the road section as the elementary matching unit, the thesis divided it into mesh-type section, tree-type section and independent section and made full use of the corresponding structure characteristics based on different section type in matching progress. A data organization method of hierarchical structure of road networks for data matching is proposed. The adjacent sections of selected road section to be matched are recognized and constructed the local road networks to be matched on the basis of spatial structure characteristic of road networks. Several potential matching local road networks are constituted according to the method of synchronous search of potential matching candidates, and then the corresponding similarity evaluation relationships between road section and road section, local road networks and local road networks are established. The paper puts forward a matching method based on road meshes. Based on the above works, the spatial similarity evaluation model has been constructed by using local road networks as a whole, which considers not only the similarity of individual between road sections, but also the similarity of structure characteristic between local road networks, thus guaranteeing the global logical consistency of the matching results between the selected section to be matched and its adjacent sections. The experimental result has fully proved that the method is effective, with a higher matching rate and accuracy.(5) It is put forward a matching method for multi-scale vector settlement based on global consistency evaluation. Using the road as global constraints and the proximity, similarity and the same orientation which are three factors of Gestalt principle as local constraints, the pattern recognition of settlement cluster has been implemented, thus the distribution characteristics and spatial relationships among settlements are obtained. A local settlement cluster to be matched which is constituted of the selected settlement and its adjacent settlements is regarded as a whole for simultaneous search of potential matching candidate set. As a result, several potential matching local settlement clusters are derived from potential matching candidate sets. The thesis proposed a spatial similarity evaluation model for local settlement cluster, which takes into account not only the similarity of the degree of overlapping, size, orientation and other features of individual settlement, but also the similarity of structure characteristic of local settlement cluster. The experimental result has fully proved that the method can effectively overcome detrimental effects due to positional error, and identify homologous settlements with higher matching accuracy, which has not only matching relationship of 1:1 but also 1:N and M:N simultaneously.