Multi-feature-constrained mainstream identification method for mesh rivers

Abstract

The invention discloses a multi-feature-constrained network river mainstream identification method, which belongs to the field of map synthesis; mainstream identification is an important operation in the process of river system synthesis, and the accuracy of identification directly affects the quality of river system synthesis. In a river system with a large spatial range, there will be a network of rivers formed by the interweaving of river channels in some areas. The river sections in it are dense, complex in structure and similar in attributes, which greatly increases the difficulty of identifying the mainstream of the river system. The multi-feature-constrained network river mainstream identification method proposed by the present invention, firstly, builds the directed topology structure of river system data, and detects the key nodes that affect the mainstream identification, that is, redundant nodes; secondly, considers the flow direction of the river, and calculates each The effective topological boundary of redundant nodes is determined to determine its "influence domain", and a hierarchical tree is established accordingly; finally, considering the multi-feature constraints such as semantics, geometry, direction, topology, and hierarchical relationships, the optimal connection path between nodes is calculated to realize mainstream recognition.

Description

technical field [0001] The invention relates to the technical field of map synthesis, and more specifically relates to a multi-feature-constrained network river mainstream identification method. Background technique [0002] Mainstream identification is an important operation in the process of river system synthesis, and the accuracy of identification directly affects the quality of river system synthesis. In a river system with a large spatial range, there will be a network of rivers formed by the interweaving of river channels in some areas. The river sections in it are dense, complex in structure and similar in attributes, which greatly increases the difficulty of identifying the mainstream of the river system. When identifying the mainstream, the existing methods first determine the mainstream watershed by setting subjective experience thresholds, resulting in the recognition results of the mainstream relying on the set thresholds. In addition, the existing methods deter...

AI Technical Summary

Problems solved by technology

[0003] In view of this, the present invention provides a multi-feature-constrained network river mainstream identification method to solve the problem that when identifying the mainstream in the existing methods, firstly determine the mainstream basin by setting the threshold of subjective experience, resulting in the recognition result of the mainstream being dependent on It is limited to the set threshold, and can only realize the determination of the local optimal river section, and lacks the consideration of the overall spatial structure characteristics of the river network, which leads to the problem that the extraction of the mainstream is not accurate enough in some cases

Images