结合路段和网眼结构的路网模式识别方法

A Pattern Recognition Method for Road Network Combining Road Sections and Mesh Structures

道路网具有鲜明的空间分布模式,其模式识别在地图制图、地图匹配及空间查询等诸多领域均发挥着至关重要的作用。当前的路网模式识别方法多基于网眼结构与路段结构两种模型,取得了较好的识别效果,但亦存在一定局限。已有的基于网眼的识别算法往往局限于最小单元的格网模式,无法对整体规则、局部破碎的网眼群组进行模式识别;而基于路段结构的模式识别则涉及复杂的预处理与识别过程,并且仅能识别单一的路网模式。方向熵作为信息熵的一个分支,可以较好地描述地理数据的空间分布特征和规律。为此,本文结合了网眼结构在网眼规整情况下的识别优势和路段结构在分析路段排列方式方面的优势,在此基础上引入方向熵、矩形度和凹凸度等特征参量,构建了一种有效的路网模式识别算法。本文选取多个城市路网作为实验对象,通过计算出不同路网模式相对应的参量分类阈值,实现了格网模式和不规则模式路网的模式识别,且根据格网模式路网中网眼群组排列方式的不同,构建网眼质心最小生成树并使用方向熵将其细分为直线型网眼群组和格网型网眼群组。实验结果表明,结合路段和网眼结构的路网模式识别算法的模式分类精度达到了97%以上,能够有效完成典型路网模式的识别并较好地根据网眼群组的排列方式完成了格网模式的细分。与现有路网模式识别方法比较,本文构建了一种简单快速且较为精准的路网模式识别算法,为地图综合、模式识别和城市规划等领域的后续研究和应用提供了一种新的思路。

The road network has a distinct spatial distribution pattern,and its pattern recognition plays a crucial role in many fields such as mapping,map matching,and spatial queries.The current road network pattern recognition methods are mostly based on two models:mesh structure and road segment structure,which have achieved good recognition results,but also have certain limitations.Existing mesh based recognition algorithms are often limited to the grid pattern of the smallest unit,and cannot recognize patterns of globally regular and locally fragmented mesh groups;Pattern recognition based on road segment structure involves complex preprocessing and recognition processes,and can only recognize a single road network pattern.As a branch of information entropy,directional entropy can effectively describe the spatial distribution characteristics and patterns of geographic data.Therefore,this article combines the recognition advantages of mesh structure in the case of regular mesh and the advantages of road section structure in analyzing the arrangement of road sections.Based on this,feature parameters such as directional entropy,rectangularity,and concavity are introduced to construct an effective road network pattern recognition algorithm.This article selects the road networks of Wuhan,Shanghai,Nanjing,and Xi'an as experimental objects for sample construction.The sample sets of Nanjing and Xi'an are used to calculate the parameter classification thresholds corresponding to different road network modes and determine the road network pattern recognition rules.The sample sets of Wuhan and Shanghai are used to verify the feasibility and transferability of the algorithm proposed in this article.This method not only completes the pattern recognition of grid networks and irregular road networks,but also divides them into linear mesh groups and grid mesh groups based on the different arrangement of mesh groups in grid networks by constructing a minimum mesh centroid spanning tree and using directional entropy.The experimental results show that the pattern classification accuracy of the road network pattern recognition algorithm combining road segments and mesh structures reaches over 97%,which can effectively complete the pattern recognition of grid networks and irregular road networks,and can well subdivide grid patterns according to the arrangement of mesh groups in the grid network.The algorithm in this paper has good transferability and the recognition results are consistent with human cognition.Compared with existing road network pattern recognition methods,this paper constructs a simple,fast,and relatively accurate road network pattern recognition algorithm,providing a new approach for subsequent research in fields such as map synthesis,pattern recognition,and urban planning.