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浙江大学学报(工学版)  2021, Vol. 55 Issue (6): 1083-1089    DOI: 10.3785/j.issn.1008-973X.2021.06.008
交通工程、土木工程     
基于无人机倾斜摄影的嘉兴老城区住宅识别
赵宇1(),朱志忠1,黄博1,*(),费佳宁2,蒋建群1
1. 浙江大学 建筑工程学院,浙江 杭州 310058
2. 宝略科技(浙江)有限公司,浙江 宁波 315100
Identification of residential buildings in Jiaxing Old City based on unmanned aerial vehicle oblique photogrammetry
Yu ZHAO1(),Zhi-zhong ZHU1,Bo HUANG1,*(),Jia-ning FEI2,Jian-qun JIANG1
1. College of Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
2. PopSmart Technology Limited Company, Ningbo 315100, China
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摘要:

为了应对老城区房屋数据信息不足、获取不便的问题,提出利用无人机(UAV)倾斜摄影测量技术实现老城区住宅自动识别的方法. 根据老城区典型多层住宅的外立面特征,确定出阳台构造、立面朝向和房屋长宽比3个参数控制的住宅判别标准. 通过无人机摄影测量获取嘉兴研究区的密集匹配点云、数字正射影像(DOM)和数字表面模型(DSM)数据. 融合DOM及DSM数据提取单体建筑轮廓,分割出单体房屋点云. 基于RANSAC算法提取房屋立面点云并确定立面朝向,根据立面的点云空间分布判断立面长度及阳台构造. 试验表明,在研究区应用该方法识别典型住宅的准确率可以达到90%.

关键词: 倾斜摄影密集匹配点云住宅老城区阳台构造    
Abstract:

A method based on unmanned aerial vehicle (UAV) oblique photogrammetry was proposed to automatically identify residential buildings in old urban areas in order to deal with the problem that the housing data in the old urban areas are incomplete and inconvenient to obtain. A residential buildings discrimination criterion controlled by three features including the existence of balcony structure, the facade orientation and the aspect ratio was determined according to the fa?ade features of the typical muti-storey residential building in the old urban areas. The dense point clouds, digital orthophoto map (DOM) and digital surface model (DSM) were obtained by UAV photography. The DOM and DSM were used to extract the building contour, based on which the point clouds were cut into building-wise ones. The facade point clouds of each building were extracted by RANSAC algorithm, and the orientation of the building was determined. The length of facade and the existence of balcony were determined by analyzing the spatial characteristics of the point clouds. Results show that the correct rate obtained by the proposed method can reach 90% in the study area.

Key words: oblique photogrammetry    dense point cloud    residential building    old urban area    balcony structure
收稿日期: 2020-07-16 出版日期: 2021-07-30
CLC:  TU 198  
基金资助: 浙江省重点研发计划资助项目(2018C03045)
通讯作者: 黄博     E-mail: zhao_yu@zju.edu.cn;cehuangbo@zju.edu.cn
作者简介: 赵宇(1983—),男,教授,从事空间信息技术与岩土工程防灾减灾的研究. orcid.org/0000-0003-0453-1960. E-mail: zhao_yu@zju.edu.cn
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引用本文:

赵宇,朱志忠,黄博,费佳宁,蒋建群. 基于无人机倾斜摄影的嘉兴老城区住宅识别[J]. 浙江大学学报(工学版), 2021, 55(6): 1083-1089.

Yu ZHAO,Zhi-zhong ZHU,Bo HUANG,Jia-ning FEI,Jian-qun JIANG. Identification of residential buildings in Jiaxing Old City based on unmanned aerial vehicle oblique photogrammetry. Journal of ZheJiang University (Engineering Science), 2021, 55(6): 1083-1089.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.06.008        https://www.zjujournals.com/eng/CN/Y2021/V55/I6/1083

飞行参数 数值
飞行高度 100、150 m
旁向重叠率 80%
航向重叠率 80%
航线方向 与建筑主立面朝向垂直或平行
表 1  无人机现场测量参数
图 1  研究区数字表面模型
图 2  研究区域的三维点云
图 3  老城区中典型住宅
图 4  典型住宅判别流程
图 5  nDSM剔除植被的过程
图 6  房屋轮廓的提取过程
图 7  单体房屋点云(俯视图)
图 8  房屋立面点云
图 9  主立面相对点云密度直方图
层数 房屋数量 漏提取数量 提取率/%
1层 14 3 78
2层 30 2 93
3、4层 22 0 100
5、6层 59 0 100
7层及以上 41 0 100
表 2  研究区房屋轮廓的提取结果
图 10  住宅识别结果图
类别 实际数量 正确判别数 错误判别数 正确率/%
住宅 70 58 12 82.9
非住宅 91 89 2 97.8
合计 161 147 14 91.3
表 3  住宅识别正确率统计
图 11  未能识别的住宅示例
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