摘要
以正确提取城区Li DAR点云中建筑物为目标,综合利用不同类别目标点云的回波特征以及地形信息,提出了一种基于区域多次回波密度分析的Li DAR点云建筑物提取方法。首先,将点云构建不规则三角网(triangulated irregular network,TIN),获取封闭的等高线;然后,利用等高线间的拓扑关系得到等高线族区域;最后,统计每一区域的多次回波点云密度信息,通过建筑物和树木区域多次回波点云在区域密度上的巨大差异来识别建筑物点云和树木点云。研究结果表明:该方法既充分利用了建筑物表面与植被间多次回波特性的差异,又不否定建筑物边缘同样存在多次回波的现象;通过封闭的等高线自适应地检测出地物目标的轮廓,弥补了传统Li DAR建筑物提取方法的不足;该方法能够较其他方法更准确地提取建筑物。
A new strategy for the classification of raw LiDAR points in urban areas, which is based on the comprehensive utilization of echo features of different object types and terrain information, is proposed in this paper according to a regional multi-return density analysis. The main procedure of the classification of the off-terrain points begins with the construction of Triangulated Irregular Network （ TIN） , and then the region of each object is captured by the contours clustering based on the topological relations of various contours traced from the TIN. Finally, the type of the object is recognized by the statistical analysis of the regional multi-return density through the significant difference between the building region and the vegetation region. This method not only makes good use of the difference in echo features between different objects such as buildings and trees but also confirms the existence of the multi-returns on the edges of the building. At the same time, the adaptive region determination of the objects is accomplished following the contours clustering. So the proposed method can dramatically increase the classification accuracy and overcome the weakness of the traditional methods, thus being more useful to the study and application of such aspects as building reconstruction and parameters estimation of the trees. Experiments prove that the new algorithm can get an effective classification.
出处
《国土资源遥感》
CSCD
北大核心
2015年第2期29-35,共7页
Remote Sensing for Land & Resources
基金
湖南省教育厅项目"复杂环境下基于机载LiDAR点云的建筑物检测方法研究"(编号:13C325)
国家自然科学青年基金项目"山地城市环境下等高线辅助的机载LiDAR点云复杂建筑物三维模型重建方法研究"(编号:41401497)共同资助
关键词
LI
DAR
数学形态学
点云分类
等高线族
区域多次回波点密度
LiDAR
mathematical morphology
point clouds classification
contour clusters
regional multi-return points density
