基于重要值排序和自适应阈值的Douglas-Peucker算法  

Douglas-Peucker algorithm based on important value sorting and adaptive threshold

作　　者：姚砺[1] 权泰宇 万燕[1] YAO Li;QUAN Taiyu;WAN Yan(School of Computer Science and Technology,Donghua University,Shanghai 201620,China)

机构地区：[1]东华大学计算机科学与技术学院,上海201620

出　　处：《智能计算机与应用》2024年第5期101-106,共6页Intelligent Computer and Applications

摘　　要：矢量数据压缩长久以来一直是地理信息系统(GIS)领域的关注焦点,旨在缩减数据规模以满足应用性能方面的需求,同时减少数据传输、系统处理时长以及储存成本,从而提高系统性能和运行开销。虽然在该问题上已取得了一些研究成果,但由于技术进步和新需求的涌现,对矢量数据压缩的压缩率和精度都提出了更高的要求,同时如何确定最优的阈值也成为了一个亟待解决的问题。因此,本文从矢量数据中不同节点对整个矢量图形产生变化的重要性以及如何确定最佳阈值出发,设计了结合重要值排序和自适应阈值的Douglas-Peucker算法。通过对上海市民政部门内部矢量数据集的实验表明,改进算法在压缩率相同情况下,数据压缩效果整体优于Douglas-Peucker算法及其改进算法。Vector data compression has long been a focal point in the Geographic Information System(GIS)field,aiming to reduce data size to meet application performance requirements,while also minimizing data transmission,system processing time and storage costs,ultimately enhancing system performance and operational efficiency.Although some research results have been achieved in this problem,higher requirements for compression rate and accuracy have been put forward due to technological progress and new demands.At the same time,how to determine the optimal threshold has become a problem to be solved.Therefore,this paper proposes the Douglas-Peucker algorithm based on importance ranking and adaptive threshold from the importance of different nodes in vector data that cause changes in the entire vector graphics and how to determine the optimal threshold.The experiment on the internal vector dataset of the Shanghai Civil Affairs Bureau shows that,under the same compression rate,the improved algorithm has an overall better data compression effect than both the Douglas-Peucker algorithm and its improved algorithms.

关 键 词：矢量数据压缩 节点重要值排序 自适应阈值 

分 类 号：P208[天文地球—地图制图学与地理信息工程]