基于Ripley K函数的景观聚集程度仿真  

Landscape Aggregation Degree Simulation Based on Ripley K Function

作　　者：罗小娇[1] 彭黎君[2] LUO Xiao-jiao;PENG Li-jun(City College,Southwest University of Science and Technology,Mianyang Sichuan 621000,China;Southwest University of Science and Technology,Mianyang Sichuan 621010,China)

机构地区：[1]西南科技大学城市学院,四川绵阳621000 [2]西南科技大学,四川绵阳621010

出　　处：《计算机仿真》2021年第5期366-370,共5页Computer Simulation

基　　金：“西南科技大学城市学院新校区校园景观设计研究”校级重点项目(2019XJXM49)。

摘　　要：为提高城市景观视觉效果以及规划合理性,提出基于RipleyK函数的景观聚集程度模拟方法。利用RipleyK函数获取景观城市尺度空间等多种限制下的格局分析,确定需求层次结构,建立景观均值图像数据,筛选有用数据,完成图像去噪。利用景观仿真形态学,划分景观图像布局。最后汇总当前景观布局指数并构建指数集,录入到PC端上,获取的输出结果,即为景观聚集程度仿真结果。实验证明,所提方法的景观聚集程度仿真结果的整体合理性系数更高,真实度更强,具有更好的实用性。In order to improve the visual effect and planning rationality of urban landscape, this article presented a method to simulate landscape aggregation degree based on RiplyK function. First, the RiplyK function was used to obtain pattern analysis of landscape scale space, under different constraints, to determine the hierarchy of needs. Second, the mean value data of landscape images were established and useful data were selected, and thus the image noise removal was completed. Moreover, the landscape image layout was divided using simulation morphology. Finally, the current landscape layout indexes were summarized to set up an index set, which was input to PC. Thus, the output result was the simulation result of landscape aggregation. Experimental results prove that the proposed method has a higher overall rational coefficient, higher truth, and better practicability in the simulation of landscape aggregation degree.

关 键 词：景观聚集 分析数据 图像筛选 

分 类 号：TP272[自动化与计算机技术—检测技术与自动化装置]