基于无人机LiDAR点云栅格化和Mask R-CNN算法的单木树冠分割  

Segmenting single tree crown using rasterization of UAV LiDAR point cloud and mask R-CNN

作　　者：廖福兰 林文树 刘浩然 LIAO Fulan;LIN Wenshu;LIU Haoran(College of Mechanical and Electrical Engineering,Northeast Forestry University,Harbin 150040,China)

机构地区：[1]东北林业大学机电工程学院,哈尔滨150040

出　　处：《农业工程学报》2024年第23期258-266,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金资助项目(31971574)。

摘　　要：精准的单木分割是进行森林结构参数提取的关键过程,也是评估森林生物量与碳储量的先决条件。为提高基于无人机LiDAR点云数据的单木分割精度,该研究提出点云栅格化处理结合深度学习算法进行单木树冠分割。首先对样地点云栅格化处理,将点云信息映射到栅格图像的RGB通道中。其次,改进Detectron2框架下的Mask R-CNN模型,在主干网络ResNet中融合GC(global context network)与CBAM(convolutional block attention module)模块。改进后模型平均精度为82.91%,相较原模型平均精度提高6.19个百分点,相较U-Net和DeepLab v3+模型平均精度分别提高7.27和4.62个百分点。最后,在测试样地中,基于点云栅格化处理结合Mask R-CNN模型的召回率R为81.19%,精确率P为78.85%,调和值F为80%,均高于分水岭算法和K-means算法。试验表明,该方法提高了单木树冠分割的准确性,为森林资源调查、生物量以及碳储量估计提供了可靠的基础数据。Single-tree segmentation can greatly contribute to the extraction of forest structure parameters.The LiDAR point cloud from unmanned aerial vehicle(UAV)was a commonly used data source for the single tree segmentation.However,the point cloud data was large,and the processing was complex.This study aimed to accurately segment the single-tree crown from the point cloud data using deep learning.The main purpose was to improve the processing of point cloud data and the accuracy of single tree segmentation in complex stands.Point cloud rasterization was combined with deep learning to segment the canopy of a single tree.Firstly,the D2000 UAV(Feima Robotics company)platform equipped with the LiDAR sensor(DLiDAR2000)was used to acquire the point cloud data of a mixed coniferous and broadleaf forest.Lidar360 software was then employed for point cloud denoising,ground point classification,and point cloud normalization preprocessing.Subsequently,the top-down rasterization of the sample plot cloud was performed to calculate the maximum height,maximum intensity,and density information per unit rasterized area of the point cloud.The RGB channels were then mapped corresponding to the rasterized image pixels,in order to make the tree canopy clearer in the rasterized image.Secondly,according to the Mask RCNN model within the Detectron2 framework,the number of layers and iterations of different backbone networks were compared to select a backbone network that provided superior segmentation performance.Thirdly,the Global Context Network(GC)and Attention Mechanism modules were integrated into the ResNet network.A comparison was made with the simultaneous introduction of the GC Net and Attention Mechanism modules to enhance the segmentation accuracy of the Mask R-CNN model.To validate the practicality of the improved Mask R-CNN model,its segmentation accuracy was compared with that of similar deep learning networks(U-Net and DeepLabv3+).Finally,the tree crown masks that were segmented by the improved Mask R-CNN were used to segment th

关 键 词：无人机 激光雷达 单木分割 点云数据 Mask R-CNN GC Net 注意力机制 

分 类 号：TN958.98[电子电信—信号与信息处理]