基于样本加权PointNet++的输电通道点云分类研究

输电通道内地物要素复杂,机载LiDAR获取的电力线、杆塔、植被等地物点云密度差异大、空间分布不规则,实际应用中"所见即所得"的应用需求对点云的高效自动化分类带来挑战.将深度学习中的PointNet++算法用于输电通道机载点云自动分类研究,分析样本加权对不同密度点云数据分类精度的影响,利用两组实验数据验证算法的精度和效率...

LiDAR Point Cloud Classification of Transmission Corridor based on Sample Weighted-PointNet++

Due to the irregular spatial distribution， various density of point cloud data and the complexity of power scenarios， the application requirements of "what you see is what you get" in practical applications and higher requirements are put forward for automatic point cloud classification. In this paper， PointNet++ algorithm of deep learning is applied to the classification of airborne LiDAR point cloud in transmission corridor， and the end-to-end automatic point cloud classification is achieved. At the same time， the effect of sample weighting on classification accuracy is analyzed. Two test datasets are used to verify the accuracy and efficiency of the proposed algorithm， and compared with the results from random forest algorithm. The experimental results show that the algorithm based on sample weighted-PointNet++ is suitable for transmission corridor point cloud classification and reaches 87.14% on the macro average F score. Moreover， the classification performance and time-consuming are better than that of random forest.