结合超体素和图优化的激光点云树木分割Integration supervoxel and graph-based optimization for instance segmentation of trees from MLS point clouds
李启才,赵闯姓
摘要(Abstract):
针对在复杂城市环境中难以有效自动提取树木信息的问题,该文首先基于移动激光扫描(MLS)数据,利用超体聚类对点云数据结构进行组织管理;然后从超体素局部上下文信息中提取去趋势几何特征,结合去趋势几何上下文特征,采用随机森林分类器对树木进行初始语义标记;接着,基于局部上下文信息进行迭代正则化,在全局图模型上进行整体优化,从而对初始语义分类结果进行空间平滑;最后根据语义标记结果,基于图割算法实现单木分割。该方法基于超体素的结构可以有效地保持场景中目标的几何边界,而且提升了处理效率。去趋势几何特征可以克服局部上下文中的冗余和显著性信息,使得获取的特征更具代表性。实验结果表明,该方法在3个数据集的树木语义标记结果达到90%左右,对结构简单且稀疏分布的树木都能正确提取。
关键词(KeyWords): 点云分割;移动激光扫描系统;超体聚类;图割算法;局部上下文特征;单木分割
基金项目(Foundation):
作者(Author): 李启才,赵闯姓
DOI: 10.16251/j.cnki.1009-2307.2020.09.018
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