作为城市主体，建筑物信息的提取一直是国内外学者研究的热点。针对目前机载激光点云数据量大、建筑点云提取不完整等难题，提出一种面向对象构建决策树的建筑点云高精度提取方法。决策树可以同时处理多种数据属性，并且对缺失值不敏感，利用点云中每个对象属性与对应各个特征值之间的映射关系，结合每个激光脚点与其邻域关系、高程均值等特征，为决策树每个内部节点生成建筑物点的判定条件，然后比较所有分类特征对应的点集不确定性（熵），确定最优特征及最优候选值，有监督地从样本数据中学习得到正确的分类器，进而完成待处理点云中建筑物点的高精度提取。实验结果表明，本文方法能够从机载激光点云数据中有效提取建筑物点，准确率可达96%。

As an urban subject, the extraction of buildings has been a hot topic for scholars. Airborne laser scanning data collected in urban areas have a huge data volume and numerous objects with complex and incomplete structures which raise a great challenge for automatic extraction of buildings. To address this challenge, we propose an algorithm based on object-oriented decision tree to extract buildings with high precision. It can handle multiple attributes simultaneously and be unaffected by missed points. First, combined with mean elevation and neighbor within each laser point, judging criteria in each internal node are determined to generate building points by using the relationship between each object attribute and its corresponding eigenvalues. Next, through comparing the entropy from all dataset features, an optimal feature and value candidate are chosen to get a correct classifier with supervised learning to apply to the dataset to be processed. The experimental results show that the proposed method is capable of extracting building points from the airborne laser scanning data with a high accuracy of above 96%.