针对激光雷达测量技术现有数据特征单一、地物辨识能力粗糙、类别划分区间模糊导致地物分类精度低的问题，提出了一种基于复合衍生特征和模糊Dempster-Shafer（DS）证据合成理论的地物分类方法。首先，确定 LiDAR数据分类特征对不同类型地物的可识别性，选择特征空间中关联性强且区分度大的源特征与衍生特征；然后，比较归一化差值植被指数与绿色归一化差值植被指数对地物反应属性的差异性，提出并构造具有高辨识能力的复合衍生特征复合归一化差值植被指数；最后，结合使用岭型信任分配函数进行模糊DS证据合成与决策，最终实现对地物的精确分类。实验结果表明，总分类精度由85.78%提高到了89.20%，证明了本文方法的有效性。

In view of the problems of single data features， rough feature recognition ability， and blurred classification interval of lidar measurement technology， a ground object classification method based on compound derivative features and fuzzy Dempster-Shafer（DS）evidence synthesis theory was proposed. First， determine the recognizability of LiDAR data classification features for different types of features， and select source and derivative features with strong correlation and high discrimination in the feature space. Then， we compare the difference of the normalized difference vegetation index with green normalized difference vegetation index to ground reaction properties， and propose and construct a compound derivative feature compound normalized difference vegetation index with high identification ability. Finally， the combination of ridge-type trust allocation function performs fuzzy DS evidence synthesis and decision-making and achieves accurate classification of the ground. The experimental results show that the total classification accuracy is improved from 85.78% to 89.20%， which proves the effectiveness of the proposed method.