提出了一种从车载激光扫描数据中层次化提取多类型目标的有效方法。该方法首先利用颜色、激光反射强度、空间距离等特征,生成多尺度超级体素;然后综合超级体素的颜色、激光反射强度、法向量、主方向等特征利用图分割方法对体素进行分割;同时计算分割区域的显著性,以当前显著性最大的区域为种子区域进行邻域聚类得到目标;最后结合聚类区域的几何特性判断目标可能所属的类别,并按照目标类别采用不同的聚类准则重新聚类得到最终目标。试验结果表明,该方法成功地提取出建筑物、地面、路灯、树木、电线杆、交通标志牌、汽车、围墙等多类目标,目标提取的总体精度为92.3%。
This paper proposes an efficient method to extract multiple objects from mobile laser scanning data. The proposed method firstly generates multi-scale supervoxels from 3D point clouds using colors, intensities and spatial distances. Then, a graph-based segmentation method is applied to segment the supervoxels by integrating their colors, intensities, normal vectors, and principal directions. Then, the saliency of each segment is calculated and the most salient segment is selected as a seed to cluster for objects clustering. Hence, the objects are classified and the constraint conditions of object's category are included to re-clustering for more accurate extraction of objects. Experiments show that the proposed method has a promising solution for extracting buildings, ground, street lamps, trees, telegraph poles, traffic signs, cars, enclosures and the objects extraction overall accuracy is 92.3%.
[1] LU Xiushan, LI Qingquan, FENG Wenhao, et al. Vehicle-borne Urban Information Acquisition and 3D Modeling System [J]. Journal of Wuhan University of Hydraulic and Electric Engineering, 2003, 36(3): 76-80. (卢秀山, 李清泉, 冯文灏, 等. 车载式城市信息采集与三维建模系统[J]. 武汉大学学报:工学版, 2003, 36(3): 76-80.)
[2] WANG Yanmin, GUO Ming. A Combined 2D and 3D Spatial Indexing of Very Large Point-cloud Data Sets [J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(4): 605-612. (王晏民, 郭明. 大规模点云数据的二维与三维混合索引方法[J]. 测绘学报, 2012, 41(4): 605-612.)
[3] WU Hangbin, LI Nan, LIU Chun, et al. Airborne LiDAR Data Segmentation Based on 3D Mathematical Morphology[J]. Journal of Remote Sensing, 2011, 15(6): 1189-1201. (吴杭彬, 李楠, 刘春,等. 机载激光扫描数据分割的三维数学形态学模型[J]. 遥感学报, 2011, 15(6): 1189-1201.)
[4] YANG B S, DONG Z. A Shape-based Segmentation Method for Mobile Laser Scanning Point Clouds [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 81: 19-30.
[5] LI Yijing, HU Xiangyun, ZHANG Jianqing, et al. Automatic Road Extraction in Complex Scenes Based on Information Fusion from LiDAR Data and Remote Sensing Imagery[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6): 870-876. (李怡静, 胡翔云, 张剑清, 等. 影像与LiDAR数据信息融合复杂场景下的道路自动提取[J]. 测绘学报, 2012, 41(6): 870-876.)
[6] LEHTOMKI M, JAAKKOLA A, HYYPP J, et al. Detection of Vertical Pole-like Objects in a Road Environment Using Vehicle-based Laser Scanning Data[J]. Remote Sensing, 2010, 2(3): 641-664.
[7] CABO C, ORDOEZ C, GARCA-CORTS S, et al. An Algorithm for Automatic Detection of Pole-like Street Furniture Objects from Mobile Laser Scanner Point Clouds [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 47-56.
[8] YANG Yun, SUI Lichun. Object-oriented Classification of LiDAR Data Based on Multi-feature Fusion [J]. Bulletin of Surveying and Mapping, 2010(8): 11-15. (杨耘, 隋立春. 面向对象的LiDAR数据多特征融合分类[J]. 测绘通报, 2010(8): 11-15.)
[9] TAN Ben, ZHONG Ruofei, LI Qin. Objects Classification with Vehicle-borne Laser Scanning Data [J]. Journal of Remote Sensing, 2012, 16(1): 50-66. (谭贲, 钟若飞, 李芹. 一种车载激光扫描数据的地物分类方法[J]. 遥感学报, 2012, 16(1): 50-66.)
[10] YANG Bisheng, DONG Zhen, WEI Zheng, et al. Extracting Complex Building Facades from Mobile Laser Scanning Data [J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3): 411-417. (杨必胜, 董震, 魏征, 等. 从车载激光扫描数据中提取复杂建筑物立面的方法[J]. 测绘学报, 42(3):411-417.)
[11] PU S, RUTZINGER M, VOSSELMAN G, et al. Recognizing Basic Structures from Mobile Laser Scanning Data for Road Inventory Studies[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6): S28-S39.
[12] XU S, VOSSELMAN G, ELBERINK S O. Multiple-entity Based Classification of Airborne Laser Scanning Data in Urban Areas [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 88: 1-15.
[13] YANG B, FANG L, LI J. Semi-automated Extraction and Delineation of 3D Roads of Street Scene from Mobile Laser Scanning Point Clouds [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 79: 80-93.
[14] MANANDHAR D, SHIBASAKI R. Auto-extraction of Urban Features from Vehicle-borne Laser Data[C]//The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2002, 34(4): 650-655.
[15] YANG B S, WEI Z, LI Q, et al. Automated Extraction of Street-scene Objects from Mobile LiDAR Point Clouds[J]. International Journal of Remote Sensing, 2012, 33(18): 5839-5861.
[16] YANG B, FANG L, LI Q, et al. Automated Extraction of Road Markings from Mobile LiDAR Point Clouds [J]. Photogrammetric Engineering & Remote Sensing, 2012, 78(4): 331-338.
[17] LIM E H, SUTER D. 3D Terrestrial LiDAR Classifications with Super-voxels and Multi-scale Conditional Random Fields [J]. Computer-aided Design, 2009, 41(10): 701-710.
[18] AIJAZI A K, CHECCHIN P, TRASSOUDAINE L. Segmentation Based Classification of 3D Urban Point Clouds: A Super-voxel Based Approach with Evaluation [J]. Remote Sensing, 2013, 5(4): 1624-1650.
[19] ACHANTA R, SHAJI A, SMITH K, et al. SLIC Superpixels Compared to State-of-the-art Superpixel Methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274-2282.
[20] LALONDE J F, VANDAPEL N, HUBER D F, et al. Natural Terrain Classification Using Three-dimensional LiDAR Data for Ground Robot Mobility [J]. Journal of Field Robotics, 2006, 23(10): 839-861.
[21] DEMANTK J, MALLET C, DAVID N, et al. DimensionalityBased Scale Selection in 3D LiDAR Point Clouds [C]//International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences.Calgary, Canada: [s.n.], 2011.
[22] FELZENSZWALB P F, HUTTENLOCHER D P. Efficient Graph-based Image Segmentation [J]. International Journal of Computer Vision, 2004, 59(2): 167-181.
