摘要
将道路网络空间视为嵌在2D空间中的独立子空间,利用形态单一的线性单元剖分图结构的边,实现网络空间的栅格化;提取网格模式的典型特征,包括几何和拓扑特征,以栅格单元邻域为目标计算特征值,构建特征向量描述栅格单元,实现对象空间到特征空间的映射,构建空间向量场;基于支持向量机（support vector machine,SVM）实现网格模式分类;结合格式塔原则完善实验结果。将此方法应用于深圳市路网数据,实验结果表明能有效地识别网格模式。
A vector tessellation method is proposed for grid pattern recognition in street networks.This study regards a street network as an independent subspace embedded in the 2 Dspace,and subdivides street segments into linear elements with equal lengths.The characteristics of grid patterns are extracted,including directional,geometrical and topological features.To map the object space to the feature space and to build a vector field,the linear element is described as a feature vector and the eigenvalues are calculated with the neighboring elements.A grid pattern classification is realized based on a support vector machine（SVM）,and the classification result is optimized based on Gestalt principles.The method was applied to the street network of Shenzhen.The experimental results show that the method effectively mines grid pattern in street networks.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2018年第1期138-144,共7页
Geomatics and Information Science of Wuhan University
基金
国家自然科学基金重点项目(41531180)
国家高技术研究发展计划(863计划)(2015AA1239012)
武汉大学研究生自主科研项目(2015205020202)~~
关键词
道路网络空间
网格模式
空间剖分
特征提取
SVM
street network space
grid pattern
spatial tessellation
feature extraction
SVM
