基于无人机倾斜摄影的嘉兴老城区住宅识别

doi:10.3785/j.issn.1008-973X.2021.06.008

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

全文: PDF(1308 KB) HTML

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

	服务
	把本文推荐给朋友
	加入引用管理器
	E-mail Alert
	作者相关文章
	赵宇
	朱志忠
	黄博
	费佳宁
	蒋建群

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 或 https://www.zjujournals.com/eng/CN/Y2021/V55/I6/1083

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

表 2 研究区房屋轮廓的提取结果

图 10 住宅识别结果图

表 3 住宅识别正确率统计

图 11 未能识别的住宅示例

1	WU S, QIU X, USERY, et al Using geometrical, textural, and contextual information of land parcels for classification of detailed urban land use[J]. Annals of the Association of American Geographers, 2009, 99 (1): 76- 98 doi: 10.1080/00045600802459028
2	LU Z, IM J, RHEE J, et al Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data[J]. Landscape and Urban Planning, 2014, 130: 134- 148 doi: 10.1016/j.landurbplan.2014.07.005
3	LU Z, IM J, QUACKENBUSH L A volumetric approach to population estimation using LiDAR remote sensing[J]. Photogrammetric Engineering and Remote Sensing, 2011, 77 (11): 1145- 1156 doi: 10.14358/PERS.77.11.1145
4	NEX F, REMONDINO F UAV for 3D mapping applications: a review[J]. Applied Geomatics, 2014, 6 (1): 1- 15 doi: 10.1007/s12518-013-0120-x
5	沈蔚, 李京, 陈云浩, 等基于LIDAR数据的建筑轮廓线提取及规则化算法研究[J]. 遥感学报, 2008, 12 (5): 692- 698 SHEN Wei, LI Jing, CHEN Yun-hao, et al Algorithms study of building boundary extraction and normalization based on LIDAR data[J]. Journal of Remote Sensing, 2008, 12 (5): 692- 698
6	YU B, LIU H, WU J, et al Automated derivation of urban building density information using airborne LiDAR data and object-based method[J]. Landscape and Urban Planning, 2010, 98 (3): 210- 219
7	RUTZINGER M, ROTTENSTEINER F, PFEIFER N A comparison of evaluation techniques for building extraction from airborne laser scanning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2009, 2 (1): 11- 20 doi: 10.1109/JSTARS.2009.2012488
8	闫利, 魏峰利用密集匹配点云的建筑单体提取算法研究[J]. 中国激光, 2018, 45 (7): 270- 277 YAN Li, WEI Feng Single part of building extraction from dense matching point cloud[J]. Chinese Journal of Lasers, 2018, 45 (7): 270- 277
9	AWRANGJEB M, RAVANBAKHSH M, FRASER C Automatic detection of residential buildings using LIDAR data and multispectral imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65 (5): 457- 467 doi: 10.1016/j.isprsjprs.2010.06.001
10	GEVAERT C, PERSELLO C, SLIUZAS R, et al Informal settlement classification using point-cloud and image-based features from UAV data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 125: 225- 236 doi: 10.1016/j.isprsjprs.2017.01.017
11	TSE R, GOLD C, KIDNER D. Using the delaunay triangulation/voronoi diagram to extract building information from raw LIDAR data[C]// Proceedings of the 4th International Symposium on Voronoi Diagrams in Science and Engineering. Glamorgan: IEEE, 2007: 222–229.
12	ALEXANDER C, SMITH-VOYSEY S, JARVIS C, et al Integrating building footprints and LiDAR elevation data to classify roof structures and visualize buildings[J]. Computers, Environment and Urban Systems, 2009, 33 (4): 285- 292 doi: 10.1016/j.compenvurbsys.2009.01.009
13	戴玉成. 基于UAV倾斜影像匹配点云的城市建筑物信息提取方法研究[D]. 北京: 中国科学院大学, 2017. DAI Yu-cheng. Urban building detection and reconstruction from point clouds derived from UAV oblique imagery[D]. Beijing: University of Chinese Academy of Sciences, 2017.
14	YANG L, SHENG Y, WANG B 3D reconstruction of building facade with fused data of terrestrial LiDAR data and optical image[J]. Optik, 2016, 127 (4): 2165- 2168 doi: 10.1016/j.ijleo.2015.11.147
15	魏征. 车载LiDAR点云中建筑物的自动识别与立面几何重建[D]. 武汉: 武汉大学, 2012. WEI Zheng. Automated extraction of buildings and facades reconstruction from mobile lidar point clouds[D]. Wuhan: Wuhan University, 2012.
16	王琛. 我国集合住宅阳台政策规定对生活需求影响分析研究[D]. 北京: 清华大学, 2011. WANG Chen. Theoretical analysis and study on balcony policy and regulations interaction to living requirement[D]. Beijing: Tsinghua University, 2011.
17	吕俊华, 彼得·罗, 张杰. 中国现代城市住宅 1840-2000[M]. 北京: 清华大学出版社, 2005.
18	胡仁禄, 周燕珉. 居住建筑设计原理[M]. 北京: 中国建筑工业出版社, 2007: 137-157.
19	杨洋, 张永生, 马一薇基于LIDAR数据的建筑物轮廓提取[J]. 测绘科学, 2010, 35 (3): 203- 205 YANG Yang, ZHANG Yong-sheng, MA Yi-wei The study on building boundary extraction based on LIDAR data[J]. Science of Surveying and Mapping, 2010, 35 (3): 203- 205
20	穆超, 余洁, 许磊基于高分辨率遥感影像的DSM建筑物点的提取研究[J]. 武汉大学学报: 信息科学版, 2009, 34 (4): 414- 417 MU Chao, YU Jie, XU Lei Research on extracting building points from the DSM data combining the high-resolution remote sensing image[J]. Geomatics and Information Science of Wuhan University, 2009, 34 (4): 414- 417 doi: 10.3321/j.issn:1671-8836.2009.04.009
21	MOUNTRAKIS G, IM J, OGOLE C Support vector machines in remote sensing: a review[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66 (3): 247- 259 doi: 10.1016/j.isprsjprs.2010.11.001
22	谭琨, 杜培军基于支持向量机的高光谱遥感图像分类[J]. 红外与毫米波学报, 2008, (2): 123- 128 TAN Kun, DU Pei-jun Hyperspectral remote sensing image classification based on support vector machine[J]. Journal of Infrared and Millimeter Waves, 2008, (2): 123- 128 doi: 10.3321/j.issn:1001-9014.2008.02.010
23	SCHNABEL R, WAHL R, KLEIN R Efficient RANSAC for point-cloud shape detection[J]. Computer Graphics Forum, 2007, 26 (2): 214- 226 doi: 10.1111/j.1467-8659.2007.01016.x
24	MARTINEZ J, SORIA A, ARIAS P, et al Automatic processing of terrestrial laser scanning data of building facades[J]. Automation in construction, 2012, 22 (3): 298- 305
25	付祥钊. 夏热冬冷地区建筑节能技术[M]. 北京: 中国建筑工业出版社, 2002: 45-59.
26	赵芹. 城市居住地块形态特征研究与表述[D]. 南京: 南京大学, 2015. ZHAO Qin. Study and description of morphological characteristics of residential plots[D]. Nanjing: Nanjing University, 2015.

[1]	应小宇,秦小颖,陈佳卉,高婧,刘紫乔. 风环境视野下基于AI的高层住宅总图生成方法[J]. 浙江大学学报(工学版), 2021, 55(11): 2186-2193.
[2]	龚敏欧阳金龙葛坚. 既有住宅建筑节能改造措施及节能减排效果——以夏热冬冷地区杭州市为例[J]. J4, 2008, 42(10): 1822-1827.

Viewed

Full text

Abstract

Cited

Shared

Discussed