针对动态图卷积神经网络(dynamic graph convolutional neural network, DGCNN)聚合邻居点信息时的局限性，提出一种增强特征融合的动态图卷积神经网络模型EFF-DGCNN，并应用于机载LiDAR点云分类。该模型主要基于DGCNN提出特征增强模块和特征融合模块，对原始三维点云进行分类。首先，基于DGCNN对原始点云进行边缘卷积获取局部特征和全局特征；然后，将全局特征集成于各层的局部特征得到增强局部特征，据此凸显点云不同特征的重要性，使网络更加关注有利于分类的特征；最后，对不同增强局部特征进行特征融合得到深层次特征，从而实现点云的分类。为验证所提模型的分类性能，在GML_DataSetA数据集和ISPRS数据集分别进行了点云分类试验。试验结果表明：相比于DGCNN，所提EFF-DGCNN模型具有更好的分类能力，能更好地区分结构相似的点云。

Aiming at the limitations of dynamic graph convolutional neural network (DGCNN) on aggregating neighbor point information, a dynamic graph convolutional neural network based on enhanced feature fusion (EFF-DGCNN) model is proposed and is used for airborne LiDAR point cloud classification. The model presents the feature enhancement module and the feature fusion module based on DGCNN, which can be applied to the classification of original 3D point clouds. Firstly, the local and global features of the original point clouds are obtained by edge convolution based on DGCNN. Then, the global features are integrated into the local features of each layer to enhance the local features, so as to highlight the importance of different features of point clouds and make the network pay more attention to the features conducive to classification. Finally, different enhanced local features are fused to obtain deep features. The fused enhanced local features are used for classification of airborne LiDAR point clouds. In order to verify the classification performance of the proposed model, experiments are conducted on the GML_DataSetA dataset and ISPRS dataset. It is demonstrated that compared with DGCNN, the proposed EFF-DGCNN model has better classification ability and can better distinguish point clouds with similar structures.