顾及长尾分布的机载LiDAR点 CNN 语义分割

针对目前PointNet++系列网络模型倾向于牺牲尾类分割精度以保证全局分割精度这一现象,构建顾及数据长尾分布的机载LEDAR点云语义分割网络,主要涉及两方面内容,聚类最远点采样和空间自注意力机制下的局部特征学习。聚类最远点采样通过类内点云最远点采样、划分区域最远点采样以及基于置信度的均值漂移（Mearshift）聚类组合策略,最大程度保留尾类样本并通过循环赋权方式使每类样本均能被网络充分学习;空间自注意力机制下的局部特征学习为结合不同空间编码方式增强采样点邻域拓扑结构的学习,以利于从稀疏样本数据中完整学习目标空间结构。公开数据集实验表明,本文网络模型整体分割精度和平均F,较 PointNet++分别提升6.3%和6.6%,并优于其它6种PointNet++系列网络模型及新公布的10种网络模型,具有良好的泛化性能与应用价值。

CNN semantic segmentation of airborne LiDAR point cloud considering long-tailed distribution

Traditional 3D point semantic segmentation networks based on PointNet++ tend to sacrifice the accuracy of minority classes to maintain the overall accuracy dominated by majority classes. A new CNN is proposed to improve the segmentation accuracy of PointNet+ when processing airborne LiDAR point clouds with long-tailed distribution,which mainly consists of two aspects. The first is cluster-based farthest point sampling(FPS).Through intra-class FPS under proportional constraints, meanshift clustering based on confidence and zoning FPS combined with neighborhood compensation, the samplesof minority classes in airborne LiDAR point clouds can be retained to the maximum extent, and can be well learned by the network through re-weighting. The second is local feature learning under the spatial self-attention mechanism. By using different spatial encoding methods, a new spatial self-attention mechanism is constructed to facilitate learning the complete structure of the target from sparse sample data. Therefore, the learning ability of the network model for minority classes is improved while ensuring the good learning ability of the majority classes. Experiments on public data set show that the overall accuracy(OA)and F,score in this article have a significant improvement,which is 6.3% and 6.6% higher than those of PointNet+Compared with other 6 networks based on PointNet+ and the top10 network model in recent publications, the proposed algorithm has the best performance,good generalization ability and application value.