摘要
针对现有激光雷达(LiDAR)点云滤波方法无法有效排除数字表面模型(DSM)中数据空洞干扰的问题,提出了基于多光谱数据指导的偏度平衡点云滤波方法。该方法将多光谱数据引入点云滤波并将其作为引导图像,实现了与噪声点光谱相似点的快速去噪。实验结果表明,该方法有效排除了数据空洞对点云滤波造成的干扰,所得到的滤波误差与原有偏度平衡点云滤波方法相比减少了0.4%~0.8%;与目前流行的基于支持向量机(SVM)的滤波算法相比,该方法的误差减少了0.1%~0.4%。
Aiming at the problem that the existing light detection and ranging （LiDAR） point cloud filtering method cannot effectively exclude the data hole interference in the digital surface model （DSM）, a skewness balance point cloud filtering method based on multispectral data guidance is proposed. This method introduces the multispectral data into the point cloud filter as the guiding image to realize the fast denoising with the spectral similarity of the noise points. The experimental results show that this method can effectively eliminate the interference caused by the data hole to the point cloud filtering, and the obtained filtering error is reduced by 0.4%-0.8% compared with the original skewness point cloud filtering method. Compared with the popular filter algorithm based on support vector machines （SVM）, the error of this method is reduced by 0.1%-0.4%.
作者
韩晓峰
杨风暴
卫红
李大威
刘丹
Han Xiaofeng;Yang Fengbao;Wei Hong;Li Dawei;Liu Dan(School of Information and Communication Engineering, North University of China, Taiyuan , Shanxi 030051, China;School of Systems Engineering, University of Reading, Reading, Berkshire RG6 6AU, UK)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第4期361-365,共5页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61672472)
中北大学科学研究基金(XJJ2016024)
中北大学电子测试技术重点实验室开放基金(ZDSYSJ2015005)
关键词
遥感
点云滤波
偏度平衡
激光雷达
remote sensing
point cloud filter
skewness balance
light detection and ranging
