一种基于动态多尺度聚类的湖泊选取方法

A Lake Selection Method Based on Dynamic Multi-scale Clustering

当前湖泊选取方法多采用整体选取的形式,且难以兼顾湖泊的属性特征、分布特征和拓扑特征。通过分析和模仿制图专家人工选取湖泊的认知行为和过程,提出一种顾及分布特征和拓扑特征保持的基于动态多尺度聚类的湖泊选取方法。首先设置面积阈值以选取大面积湖泊,然后通过缓冲区选取'孤立'湖泊,接下来对湖泊群进行动态多尺度聚类来划分出湖泊分布密度不同的区域,对不同区域按开方根规律确定选取数量指标并采用不同选取策略,其中对包含湖泊数量较多的区域依据由主成分分析法定量计算出的重要性综合评价进行迭代选取,直至达到选取数量指标。实验对比表明,该方法在综合考虑重要性的前提下,有效地保持了选取前后湖泊群的形态结构和密度对比。

Current lake selection methods mostly use the form of selecting as a whole,and it is difficult for them to take into account the attribute characteristics,distribution characteristics and topological characteristics of the lake.By analyzing and imitating the cognitive behavior and process of artificial lakes selection,this paper proposes a lake selection method based on dynamic multi-scale clustering.Firstly,we set the area threshold to select the lakes with large area,then select the'isolated'lake through the buffer,then utilize the dynamic multi-scale clustering to the lake group to divide into areas with different density,decide the selection numbers by square root law and adopt the different selection strategy for the different areas,among whose lakes are selected according to the comprehensive evaluation of importance calculated by iterative principal component analysis in the lake group class with a large number of features until the number of lakes reaches the selection number.Experiments show that our proposed method maintains the morphological structure and density contrast of the lake group effectively,under the premise of considering the importance.