CAJ | 학술논문

　　点云数据滤波仍旧是现阶段机载LiDAR数据后处理的首要步骤，但其发展尚未完全成熟。在回顾和总结已有滤波算法的基础上，将统计学中偏度与峰度的概念引入到算法中，提出了一种新的基于偏度平衡的地面点与非地面点非监督分类方法，利用统计矩原理从LiDAR点云数据生成的DSM中有效地提取DTM。该方法区别传统算法的最大的优势在于无需参数或者阈值支持，并且相对于LiDAR点云数据的格式和分辨率是独立的。实验结果证明，该方法切实可行，具有较强的适应性，并且能够较好地满足精度要求。
　　점운수거려파잉구시현계단궤재LiDAR수거후처리적수요보취，단기발전상미완전성숙。재회고화총결이유려파산법적기출상，장통계학중편도여봉도적개념인입도산법중，제출료일충신적기우편도평형적지면점여비지면점비감독분류방법，이용통계구원리종LiDAR점운수거생성적DSM중유효지제취DTM。해방법구별전통산법적최대적우세재우무수삼수혹자역치지지，병차상대우LiDAR점운수거적격식화분변솔시독립적。실험결과증명，해방법절실가행，구유교강적괄응성，병차능구교호지만족정도요구。
Generally, filtering for point clouds is considered as a primary step for airborne LiDAR data post-processing. However, it is still under development. By reviewing and summarizing the existing filtering algorithms, the concepts of skewness and kurtosis in statistics are introduced to the framework, and a novel unsupervised classification algorithm to differentiate ground and non-ground points based on skewness balancing is presented. Digital Terrain Model(DTM)is effectively extracted from Digital Surface Model(DSM)generated from LiDAR data by exploiting the statistical moments principle. As an alternative to traditional approaches, the ultimate advantages of this algorithm are parameter-and threshold-freedom and independence from LiDAR data format and resolution. Experiment results show that the algorithm is highly practicable and adaptive, and can meet the required precision properly.