Building outline extraction based on airborne LiDAR data

doi:10.13474/j.cnki.11-2246.2019.0388

Abstract

Abstract: As an important element of 3D reconstruction of buildings, building contours are crucial in building smart cities and digital cities. Aiming at some problems in the extraction of point cloud filtering, building roof surface extraction, building contour extraction and extraction accuracy evaluation from the airborne LiDAR point cloud, this paper proposes a comprehensive region growth improvement algorithm, three-dimensional Hough transform algorithm and α-shape algorithm for building contour extraction method. Based on the denoising of airborne LiDAR point cloud data, the method uses an improved region growing algorithm to filter the ground points and removes the lower height features through the height threshold based on the normalized elevation features of the ground point to the ground. Then, based on the three-dimensional Hough transform algorithm, the building plane is extracted from the remaining buildings and the tall tree point cloud, and finally the contour information of the building is extracted using the α-shape algorithm. The point cloud data of a certain urban area scanned by the RIEGLVQ-1560i airborne LiDAR measurement system is calculated, and the accuracy of the extracted building outline is evaluated by the evaluation indexes such as matching degree, shape similarity and positional accuracy. The results show that the integrated region growth improvement algorithm, three-dimensional Hough transform algorithm and α-shape algorithm for building contour extraction method can accurately extract the contour information of buildings, and have the characteristics of stability and universal applicability for wide-area building contour extraction.

Key words: airborne LiDAR point cloud, building contour extraction, region growing, Hough transform, α-shape algorithm

CLC Number:

P237

ZHU Yimin, TIAN Linya, BI Jixin, LIN Song. Building outline extraction based on airborne LiDAR data[J]. Bulletin of Surveying and Mapping, 2019, 0(12): 65-70.

References

[1] 赵传, 张保明, 陈小卫, 等. 一种基于LiDAR点云的建筑物提取方法[J]. 测绘通报, 2017(2):35-39.
[2] 洪绍轩, 王竞雪. 结合OTSU与迭代三角网的机载LiDAR建筑物点云提取[J]. 遥感信息, 2018, 33(6):79-85.
[3] 杨洋, 张永生, 马一薇, 等. 基于LiDAR数据的建筑物轮廓提取[J]. 测绘科学, 2010, 35(3):203-205.
[4] 孙颖, 张新长, 罗国玮. 从机载激光雷达点云提取建筑物屋顶边界的活动轮廓模型改进方法[J]. 测绘学报, 2014,43(6):620-626.
[5] 冯茂林. 树木遮挡下的机载LiDAR点云建筑物轮廓提取[D]. 成都:西南交通大学, 2017.
[6] 李云帆, 龚威平, 林俞先, 等. LiDAR点云与影像相结合的建筑物轮廓信息提取[J]. 国土资源遥感, 2014, 26(2):54-59.
[7] 马红. 利用高精度DSM数据提取建筑物轮廓算法研究[J]. 测绘通报, 2015(4):111-113.
[8] 尤红建, 苏林, 李树楷. 利用机载三维成像仪的DSM数据自动提取建筑物[J]. 武汉大学学报(信息科学版), 2002, 27(4):408-413.
[9] WANG W, WANG J, SUN G. Noise reduction and modeling methods of TLS point cloud based on R-tree[C]//Urban Remote Sensing Event.[S.l.]:IEEE, 2009:1-5.
[10] 蒋晶珏. LiDAR数据基于点集的表示与分类[D]. 武汉:武汉大学, 2006.
[11] FANG H T, HUANG D S. Noise reduction in lidar signal based on discrete wavelet transform[J]. Optics Communications, 2004, 233(1):67-76.
[12] FORLANI G, NARDINOCCHI C, SCAIONI M, et al. Complete classification of raw LiDAR data and 3D reconstruction of buildings[J]. Pattern Analysis & Applications, 2006, 8(4):357-374.
[13] 何华, 李宗春, 李国俊, 等. 散乱点云的自适应α-shape曲面重建[J]. 计算机应用, 2016, 36(12):3394-3397.
[14] 孙殿柱, 魏亮, 李延瑞, 等. 基于局部样本增益优化的α-shape曲面拓扑重建[J]. 机械工程学报, 2016, 52(3):136-142.
[15] ZENG C, WANG J, LEHRBASS B. An evaluation system for building footprint extraction from remotely sensed data[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2013, 6(3):1640-1652.