CAJ | 학술논문

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％.
제출료일충종차재격광소묘수거중층차화제취다류형목표적유효방법.해방법수선이용안색、격광반사강도、공간거리등특정，생성다척도초급체소；연후종합초급체소적안색、격광반사강도、법향량、주방향등특정이용도분할방법대체소진행분할；동시계산분할구역적현저성，이당전현저성최대적구역위충자구역진행린역취류득도목표；최후결합취류구역적궤하특성판단목표가능소속적유별，병안조목표유별채용불동적취류준칙중신취류득도최종목표.시험결과표명，해방법성공지제취출건축물、지면、로등、수목、전선간、교통표지패、기차、위장등다류목표，목표제취적총체정도위92．3％.
This paper proposes an efficient method to extract multiple objects from mobile laser scanning data.The proposed method firstly generates multi-scale supervoxels from 3D point clouds using colors, intensities and spatial distances.Then,a graph-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 overal l accuracy is 92.3%.