一种基于相似度的点群综合选取算法  

Point Cluster Selection Algorithm Based on Similarity Degree

作　　者：李伟[1] 何晋强[1] 

机构地区：[1]中南大学地球科学与信息物理学院,湖南长沙410083

出　　处：《科技创新与生产力》2017年第3期34-37,共4页Sci-tech Innovation and Productivity

摘　　要：为了提出兼顾统计信息、拓扑信息、度量信息和专题信息的点群综合算法,笔者将4类信息分别归纳为相应的空间点群相似度指标,构建了合适的多目标函数模型,利用遗传智能算法对多目标函数进行了逐步优化,选取较优点,得到了最优值并达到了综合结果。实验结果表明,该算法能够很好地传递综合前后的关键信息,并可以利用并行计算提高效率。In order to put forward the point group synthesis algorithm considering statistical information, thematic information, metric information and topological information, the four types of information were divided into similarity index of point group and the multi-objective function model was constructed, then the multi-objective function was optimized by genetic algorithm and the optimal value and comprehensive results were obtained, Results show that the algorithm can transfer the key information well and improve the efficiency

关 键 词：点群综合 空间点群相似度 多目标优化 遗传算法 

分 类 号：P208[天文地球—地图制图学与地理信息工程]