摘要
提出了一种从车载激光扫描数据中层次化提取多类型目标的有效方法。该方法首先利用颜色、激光反射强度、空间距离等特征,生成多尺度超级体素;然后综合超级体素的颜色、激光反射强度、法向量、主方向等特征利用图分割方法对体素进行分割;同时计算分割区域的显著性,以当前显著性最大的区域为种子区域进行邻域聚类得到目标;最后结合聚类区域的几何特性判断目标可能所属的类别,并按照目标类别采用不同的聚类准则重新聚类得到最终目标。试验结果表明,该方法成功地提取出建筑物、地面、路灯、树木、电线杆、交通标志牌、汽车、围墙等多类目标,目标提取的总体精度为92.3%。
This paper proposes an efficient method to extract multiple objects from mobile laser scanning data.The proposed method firstly generates multi-scale supervoxels from 3Dpoint clouds using colors,intensities and spatial distances.Then,agraph-based segmentation method is applied to segment the supervoxels by integrating their colors,intensities,normal vectors,and principal directions.Then,the saliency of each segment is calculated and the most salient segment is selected as a seed to cluster for objects clustering.Hence,the objects are classified and the constraint conditions of object＇s category are included to re-clustering for more accurate extraction of objects.Experiments show that the proposed method has a promising solution for extracting buildings,ground,street lamps,trees,telegraph poles,traffic signs,cars,enclosures and the objects extraction overall accuracy is 92.3%.
出处
《测绘学报》
EI
CSCD
北大核心
2015年第9期980-987,共8页
Acta Geodaetica et Cartographica Sinica
基金
国家973计划(2012CB725301)
国家自然科学基金(41071268)~~
关键词
车载激光点云
多尺度超级体素
多类型目标提取
显著性
层次化提取
mobile laser scanning
multi-scale supervoxel
multiple object extraction
saliency
hierarchical extraction
