基于UAV影像密集匹配点云多层次分割的建筑物层高变化检测  

3D Change Detection of Buildings Based on Multi-level Segmentation of Dense Matching Point Clouds from UAV Images

作　　者：杨钰琪 陈驰[1,2] 杨必胜[1,2] 胡平波[1,2] 崔扬 YANG Yuqi;CHEN Chi;YANG Bisheng;HU Pingbo;CUI Yang(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Engineering Research Center for Spatiotemporal Data Smart Acquisition and Application,Ministry of Education,Wuhan University,Wuhan 430079,China;Key Laboratory for Geo-Environment Monitoring of Coastal Zone of the National Administration of Surveying,Mapping and GeoInformation,Shenzhen Universtiry,Shenzhen 518060,China;Shenzhen Key Laboratory of Spatial Smart Sensing and Service,Shenzhen University,Shenzhen 518060,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [2]武汉大学时空数据智能获取技术与应用教育部工程研究中心,湖北武汉430079 [3]深圳大学海岸带地理环境检测国家测绘地理信息局重点实验室,广东深圳518060 [4]深圳大学空间信息智能感知与服务深圳市重点实验室,广东深圳518060

出　　处：《武汉大学学报（信息科学版）》2021年第4期489-496,共8页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(41725005,41701530,41531177);国家重点研发计划(2016YFF0103501);中国博士后科学基金特别资助(2018T110802);超大型电网三维可视化管理与时空分析技术研究资助项目(ZBKJXM20170229)。

摘　　要：针对城市建筑物层高变化检测难题,提出一种基于无人机(unmanned aerial vehicle,UAV)影像密集匹配点云多层次分割的变化检测方法。首先,对多时相UAV影像匹配密集点云进行网格划分,并计算网格内部的归一化数字表面模型和差分数字表面模型两种几何形状特征以及归一化过绿指数和亮度两种光谱特征;然后,基于区域生长规则进行点云分割,并判断分割对象的变化/未变化/不确定状态,对不确定状态的分割对象,逐步严格生长准则实现多层次迭代分割,直至判断出所有点的变化状态(增高/降低/未变化);最后,综合几何形状特征及光谱特征,识别变化对象中的三维建筑物目标以明确层高变化。采用两期武汉大学UAV影像密集点云进行实验验证,结果表明,所提检测方法的检测完整率、正确率及检测质量均达到90%以上。Objectives:Buildings are one of the main bodies of the city.Change information of buildings is of great significance to the investigation and treatment of illegal buildings,urban planning management and the real⁃time incremental updating of geographic databases.Rapid,accurate and low⁃cost methods of 3D change detection have received more and more attention.Methods:This paper proposes a method of building change detection base on dense matching point clouds from unmanned aerial vehicle(UAV)images.First,we meshed point clouds,and analyzed the space features including normalized digital surface model(nDSM)and differential digital surface model(dDSM)and the spectral features including normalized exces⁃sive green index(nEGI)and brightness in grids.Then point clouds are segmented based on region growth,and the state of the segmentation object(changing/unchanged/uncertain)is judged.The segmentation criterion will become more and more strict for the segmented object with uncertain state until the changing state of all points is judged(taller/lower/uncertain).Finally,spatial and spectral features are integrated to identify building targets from changed objects.Results:The dense matching point clouds from UAV images in two phases of Wuhan University are used to verify the experimental results of this method.The experimental re⁃sults show that the integrity,accuracy and detection quality of the proposed change detection method can all reach more than 90%.Conclusions:The proposed method can achieve object⁃level,high⁃precision 3D change detection of buildings based on multi⁃level segmentation and voting strategy.

关 键 词：无人机 密集匹配 点云 变化检测 多层次分割 

分 类 号：P237[天文地球—摄影测量与遥感]