This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information throu...This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.展开更多
目前,高压电力线巡检效率已不能满足新时期电力系统智能化管理的要求。通过深入探讨密度聚类法在机载LiDAR电力线点云提取中存在的弊端,给出自适应—密度聚类解决方案。实测青海省某电力走廊机载LiDAR点云数据,借助Visual Studio 2010 ...目前,高压电力线巡检效率已不能满足新时期电力系统智能化管理的要求。通过深入探讨密度聚类法在机载LiDAR电力线点云提取中存在的弊端,给出自适应—密度聚类解决方案。实测青海省某电力走廊机载LiDAR点云数据,借助Visual Studio 2010 C++开发环境,编制自适应—密度聚类等相关处理程序,对自适应密度聚类方案的电力线点云提取、电力线三维抛物线的拟合进行测试与精度评定,结果表明:①自适应—密度聚类方案提取准确率达99.96%,电力线拟合最小残差0.220m,最大拟合残差0.252m,平均拟合残差0.232m;②自适应—密度聚类方案一次便可成功提取电力线,较好地规避了密度聚类法中多次试探邻域半径r_(Eps)与密度阈值p_(MinPts)等初始参数的赋值问题,大大提高了基于机载LiDAR点云数据的电力巡线工作效率,可应用于电力行业的实际工作中。展开更多
对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数...对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明:算法的平均总体精度和Kappa系数分别可达90.17%和0.861,可有效实现城区MS-LiDAR数据的三维土地覆盖分类;分步处理的方式更有利于针对具体的分离目标的特点设计简单且有效的规则,算法设计更简单、复杂度低;NDRI可为其他机器学习算法的显著性特征的设计和选择提供理论支撑。展开更多
基金funded in part by the Key Project of Nature Science Research for Universities of Anhui Province of China(No.2022AH051720)in part by the Science and Technology Development Fund,Macao SAR(Grant Nos.0093/2022/A2,0076/2022/A2 and 0008/2022/AGJ)in part by the China University Industry-University-Research Collaborative Innovation Fund(No.2021FNA04017).
文摘This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.
文摘目前,高压电力线巡检效率已不能满足新时期电力系统智能化管理的要求。通过深入探讨密度聚类法在机载LiDAR电力线点云提取中存在的弊端,给出自适应—密度聚类解决方案。实测青海省某电力走廊机载LiDAR点云数据,借助Visual Studio 2010 C++开发环境,编制自适应—密度聚类等相关处理程序,对自适应密度聚类方案的电力线点云提取、电力线三维抛物线的拟合进行测试与精度评定,结果表明:①自适应—密度聚类方案提取准确率达99.96%,电力线拟合最小残差0.220m,最大拟合残差0.252m,平均拟合残差0.232m;②自适应—密度聚类方案一次便可成功提取电力线,较好地规避了密度聚类法中多次试探邻域半径r_(Eps)与密度阈值p_(MinPts)等初始参数的赋值问题,大大提高了基于机载LiDAR点云数据的电力巡线工作效率,可应用于电力行业的实际工作中。
文摘对比仅包含多光谱信息、仅可实现二维土地覆盖分类的传统光学遥感数据,机载多光谱激光雷达(multispectral light detection and ranging,MS-LiDAR)的优势在于同时包含多光谱和空间信息、可实现三维土地覆盖分类,但现有的机载MS-LiDAR数据的土地覆盖分类研究所需特征维度过高、算法复杂度高。因此,提出了一种整合空间相关性和归一化差分比率指数(Normalized Difference Ratio Index,NDRI)特征的逐步分类算法。该算法首先融合机载MS-LiDAR数据的多波段独立点云,获取兼具空间位置及其多光谱信息的单一点云数据;然后利用空间邻域增长下的地面滤波算法分离地面和非地面点;接着基于不同目标的激光反射特性差异设计将草地(树木)自地面(非地面)中分离的NDRI指数,并利用类间方差最大原则下的自适应最优NDRI指数实现地面和非地面点的精细分类;最后利用3D多数投票法优化分类结果。采用加拿大Optech Titan实测MS-LiDAR数据测试提出算法的有效性及可行性,实验结果表明:算法的平均总体精度和Kappa系数分别可达90.17%和0.861,可有效实现城区MS-LiDAR数据的三维土地覆盖分类;分步处理的方式更有利于针对具体的分离目标的特点设计简单且有效的规则,算法设计更简单、复杂度低;NDRI可为其他机器学习算法的显著性特征的设计和选择提供理论支撑。
