从灾后机载激光点云自动检测损毁房屋的等高线簇分析方法

doi:10.11947/j.AGCS.2015.20130785

测绘学报 ›› 2015, Vol. 44 ›› Issue (4): 407-413.doi: 10.11947/j.AGCS.2015.20130785

从灾后机载激光点云自动检测损毁房屋的等高线簇分析方法

何美章¹, 朱庆^1,2,3, 杜志强¹, 张叶廷¹, 胡翰¹, 林月冠⁴, 齐华^2,3

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. 高速铁路运营安全空间信息技术国家地方联合工程实验室, 四川成都 611756;
3. 西南交通大学地球科学与环境工程学院, 四川成都 611756;
4. 民政部国家减灾中心, 北京 100124

收稿日期:2013-12-20 修回日期:2014-11-25 出版日期:2015-04-20 发布日期:2015-04-27
通讯作者: 杜志强E-mail:duzhiqiang@whu.edu.cn E-mail:duzhiqiang@whu.edu.cn
作者简介:何美章(1981—),男,博士生,研究方向为激光扫描测量.E-mail:151703120@qq.com
基金资助:
国家863计划(2012AA121305);国家自然科学基金(41171311);国家高分辨率对地观测系统(民用部分)科研项目(03-Y30B06-9001-13/15);四川省科技计划项目(2014SZ0106)

Contour Cluster Shape Analysis for Building Damage Detection from Post-earthquake Airborne LiDAR

HE Meizhang¹, ZHU Qing^1,2,3, DU Zhiqiang¹, ZHANG Yeting¹, HU Han¹, LIN Yueguan⁴, QI Hua^2,3

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. State-province Joint Engineering Laboratory of Spatial Information Technology of High-speed Rail Safety, Chengdu 611756, China;
3. Faculty of Geosciences and Environmental Engineering, Southeast Jiaotong University, Chengdu 611756, China;
4. National Disaster Reduction Center of China, MCA, Beijing 100124, China

Received:2013-12-20 Revised:2014-11-25 Online:2015-04-20 Published:2015-04-27
Supported by:
The National 863 Program of China(No.2012AA121305);The National Natural Science Foundation of China(No. 41171311);The National High Resolution Earth Observation System(the Civil Part) Technology Projects of China(No.03-Y30B06-9001-13/15);The Technology Plan Projects of Sichuan(No. 2014SZ0106)

摘要/Abstract

摘要：

利用灾后机载激光扫描点云的地震损毁房屋检测方法主要针对平面屋顶房屋,从局部分析屋顶的平面特征,导致只能有效检测屋顶严重破碎的损毁房屋.为此本文提出了一种等高线簇相似分析的地震损毁房屋检测方法,充分挖掘房屋等高线簇蕴含的房屋表面形状丰富的二维和三维信息,利用等高线簇形状相似度的归一化信息熵从整体上综合描述损毁房屋的损毁特征,并利用最大熵模型自动检测损毁房屋.采用2010年4月El Mayor-Cucapah地震断裂带激光点云数据进行了试验,证明本文提出的方法能快速、准确、可靠地检测损毁房屋.

关键词: 等高线簇, 形状相似度, 信息熵, 损毁房屋检测, 震后点云

Abstract:

Detection of the damaged building is the obligatory step prior to evaluate earthquake casualty and economic losses. It's very difficult to detect damaged buildings accurately based on the assumption that intact roofs appear in laser data as large planar segments whereas collapsed roofs are characterized by many small segments. This paper presents a contour cluster shape similarity analysis algorithm for reliable building damage detection from the post-earthquake airborne LiDAR point cloud. First we evaluate the entropies of shape similarities between all the combinations of two contour lines within a building cluster, which quantitatively describe the shape diversity. Then the maximum entropy model is employed to divide all the clusters into intact and damaged classes. The tests on the LiDAR data at El Mayor-Cucapah earthquake rupture prove the accuracy and reliability of the proposed method.

Key words: contour cluster, shape similarity, entropy, damaged building detection, post-earthquake airborne LiDAR point cloud

中图分类号:

P237

何美章, 朱庆, 杜志强, 张叶廷, 胡翰, 林月冠, 齐华. 从灾后机载激光点云自动检测损毁房屋的等高线簇分析方法[J]. 测绘学报, 2015, 44(4): 407-413.

HE Meizhang, ZHU Qing, DU Zhiqiang, ZHANG Yeting, HU Han, LIN Yueguan, QI Hua. Contour Cluster Shape Analysis for Building Damage Detection from Post-earthquake Airborne LiDAR[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(4): 407-413.

参考文献

[1] CHENG Liang, GONG Jianya. Building Boundary Extraction Using Very High Resolution Images and LiDAR[J]. Acta Geodaetica et Cartographica Sinica, 2008, 37(3): 391-393, 399. (程亮, 龚健雅. LiDAR辅助下利用超高分辨率影像提取建筑物轮廓方法[J]. 测绘学报, 2008, 37(3): 391-393, 399.)
[2] SUN Ying, ZHANG Xinchang, KANG Tingjun, et al. Improved GAC Model for Automatic Building Extraction from LiDAR Point Clouds and Aerial Image[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3): 337-343, 350. (孙颖, 张新长, 康停军, 等. 改进GAC模型在点云和影像自动提取建筑物边界中的应用[J]. 测绘学报, 2013, 42(3): 337-343, 350.)
[3] LI Ying, FENG Zhongke, WANG Haiping, et al. Three-dimensional Modeling of Buildings Based on LiDAR Point Cloud[J]. Forest Inventory and Planning, 2011, 36(6): 29-31. (李影, 冯仲科, 王海平, 等. 基于LiDAR点云的建筑物的三维建模[J]. 林业调查规划, 2011, 36(6): 29-31.)
[4] CHEN Zhou, MA Hongchao. Automatic Extracting and Modeling Approach of City Cloverleaf from Airborne LiDAR Data[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2): 252-258. (陈卓, 马洪超. 基于机载LiDAR数据的大型立交桥自动提取与建模方法[J]. 测绘学报, 2012, 41(2): 252-258.)
[5] CHENG Liang. 3D Building Model Reconstruction from Imagery and LiDAR Data[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(4): 376. (程亮. 集成影像与LiDAR数据重建3维建筑物模型研究[J]. 测绘学报, 2009, 38(4): 376.)
[6] SCHWEIER C, MARKUS M. Classification of Collapsed Buildings for Fast Damage and Loss Assessment [J]. Bulletin of Earthquake Engineering, 2006, 4(2): 177-192.
[7] DONG L G, SHAN J. A Comprehensive Review of Earthquake-induced Building Damage Detection with Remote Sensing Techniques [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 84: 85-99.
[8] OLSEN M J, CHEN Z, HUTCHINSON T, et al. Optical Techniques for Multiscale Damage Assessment [J]. Geomatics, Natural Hazards and Risk, 2012, 4(1): 49-70.
[9] LABIAK R C, VAN AARDT J A, BESPALOV D, et al. Automated Method for Detection and Quantification of Building Damage and Debris Using Post-Disaster LiDAR Data[C]//Proceedings of SPIE 8037, Laser Radar Technology and Applications XVI. Orlando: SPIE, 2011: 80370F.
[10] REHOR M, BÄHR H. Segmention of Damaged Buildings from Laser Scanning Data [C]//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.Bonn, Germany: [s.n.],XXXVI, Part 3, 2006: 67-72.
[11] REHOR M, BÄHR H, TARSHA-KURDI F, et al. Contribution of Two Plane Detection Algorithms to Recognition of Intact and Damaged Buildings in LiDAR Data [J]. The Photogrammetric Record, 2008, 23(124): 441-456.
[12] ELBERINK S O, SHOKO M, FATHI S A, et al. Detection of Collapsed Buildings by Classifying Segmented Airborne Laser Scanner Data[C]//Proceedings of International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Calgary:[s.n.], 2011, XXXVIII(5/W12): 307-312.
[13] KHOSHELHAM K, ELBERINK S O. Role of Dimensionality Reduction in Segment-based Classification of Damaged Building Roofs in Airborne Laser Scanning Data[C]//Proceedings of the 4th GEOBIA. Brazil: Rio de Janeiro, 2012: 372-377.
[14] KHOSHELHAM K, OUDE ELBERINK S, SUDAN X. Segment-based Classification of Damaged Building Roofs in Aerial Laser Scanning Data [J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(5): 1258-1262.
[15] ZHANG Jing, LI Lelin, JIANG Wanshou. Contour Clustering Analysis for Building Reconstruction from LIDAR Data[J]. Journal of Geo-Information Science, 2010, 12(5): 641-648. (张靖, 李乐林, 江万寿. 基于等高线簇分析的复杂建筑物模型重建方法[J]. 地球信息科学学报, 2010, 12(5): 641-648.)
[16] JIANG Wangshou,GUO Dahai,ZHANG Jing,et al. Analysis of Airborne Laser Scanning Point Cloud for oveall Filter Bused on the Coutour of Cluster. Wuhan,201010262366[P]. 2011-02-16.(江万寿, 郭大海, 张靖, 等. 基于等高线簇分析的机载激光扫描点云整体滤波方法: 中国武汉, 201010262366[P]. 2011-02-16.)
[17] CHEN Jie, DENG Min, XU Feng, et al. A Measurement Approach for Spatial Information of Area Map[J]. Science of Surveying and Mapping, 2010, 35(1): 74-76, 49. (陈杰, 邓敏, 徐枫, 等. 面状地图空间信息度量方法研究[J]. 测绘科学, 2010, 35(1): 74-76, 49.)
[18] LIU Huimin, FAN Zide, DENG Min, et al. A Hierarchical Approach to Measuring the Information Content of the Contours in a Map[J].Acta Geodaetica et Cartographica Sinica, 2012, 41(5): 777-783. (刘慧敏, 樊子德, 邓敏, 等. 地图上等高线信息度量的层次方法研究[J]. 测绘学报, 2012, 41(5): 777-783.)
[19] LI Z L, HUANG P Z. Quantitative Measures for Spatial Information of Maps [J]. International Journal of Geographical Information Science, 2002, 16(7): 699-709.
[20] LIU Wenkai, QIAO Chaofei, CHEN Yunhao, et al. Quantitative Measures for Spatial Information of Contour Maps[J]. Geomatics and Information Science of Wuhan University, 2008, 33(2): 157-159, 196. (刘文锴, 乔朝飞, 陈云浩, 等. 等高线图信息定量度量研究[J]. 武汉大学学报: 信息科学版, 2008, 33(2): 157-159, 196.)
[21] LIU Pengcheng, LUO Jing, AI Tinghua, et al. Evaluation Model for Similarity Based on Curve Generalization[J]. Geomatics and Information Science of Wuhan University, 2012, 37(1): 114-117. (刘鹏程, 罗静, 艾廷华, 等. 基于线要素综合的形状相似性评价模型[J]. 武汉大学学报: 信息科学版, 2012, 37(1): 114-117.)
[22] LIU Mei, LIU Weidong, XU Rongqing, et al. Image Retrieval Based on Wavelet Transform and Fourier Descriptors[J]. Systems Engineering and Electronics, 2003, 25(8): 1000-1002. (刘梅, 刘伟东, 许荣庆, 等. 基于小波变换及傅里叶描述子的图像检索[J]. 系统工程与电子技术, 2003, 25(8): 1000-1002.)
[23] WAN Wei, FENG Xuezhi, XIAO Pengfeng, et al. Shape Feature Representation of Ground Objects from High-resolution Remotely Sensed Imagery Based on Fourier Descriptors[J]. Journal of Remote Sensing, 2011, 15(1): 73-87. (万玮, 冯学智, 肖鹏峰, 等. 基于傅里叶描述子的高分辨率遥感图像地物形状特征表达[J]. 遥感学报, 2011, 15(1): 73-87.)
[24] CAO Jiannong. Review on Image Segmentation Based on Entropy[J]. PR & AI, 2012, 25(6): 958-971. (曹建农. 图像分割的熵方法综述[J]. 模式识别与人工智能, 2012, 25(6): 958-971.)

从灾后机载激光点云自动检测损毁房屋的等高线簇分析方法

Contour Cluster Shape Analysis for Building Damage Detection from Post-earthquake Airborne LiDAR

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