INCREMENTAL CONTOUR FUSION BASED ON LINE/LINE TOPOLOGICAL RELATIONS

Incremental contour fusion plays a very important role in updating topographic database. The most popular fusion methods in current use depend on manual work basically, in which it results in large workload, low efficiency and it can’t ensure the quality of the spatial data. Therefore, the automatization of contour fusion should be developed exactly. Topological relation between unchanged contour and changed one is an important topic for contour fusion. In this paper, a new approach based on whole object is pursued to compute the binary topological relationship between them, in which “FL Points ” are introduced, intersection and difference operators are selected from set operators to distinguish the topological relations between neighboring spatial line objects; three types of topological invariants are used for the computational results of set operations: contents, dimension and connectivity-number. Then 14 fusion rules are concluded and a prototype system for automated fusion is implemented. The proposed approach is examined to be reasonable and practicable by real and simulated experimental data. 1

Automatic drainage pattern recognition in river networks

In both geographic information system and terrain analysis, drainage systems are important components. Owing to local topography and subsurface geology, a drainage system achieves a particular drainage pattern based on the form and texture of its network of stream channels and tributaries. Although research has been done on the description of drainage patterns in geography and hydrology, automatic drainage pattern recognition in river networks is not well developed. This article introduces a new method for automatic classification of drainage systems in different patterns. The method applies to river networks, and the terrain model is not required in the process. A series of geometric indicators describing each pattern are introduced. Network classification is based on fuzzy set theory. For each pattern, the level of membership of the network is given by the different indicator values. The method was implemented, and the experimental results are presented and discussed