Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm

作者:Dai, YC (Dai, Yucheng)[ 1,2 ] ; Gong, JH (Gong, Jianhua)[ 1 ] ; Li, Y (Li, Yi)[ 1 ] ; Feng, QL (Feng, Quanlong)[ 1,3 ]

INTERNATIONAL JOURNAL OF DIGITAL EARTH

卷: 10  期: 11  页: 1077-1097

DOI: 10.1080/17538947.2016.1269841

出版年: 2017

摘要

This paper presents an approach to process raw unmanned aircraft vehicle (UAV) image-derived point clouds for automatically detecting, segmenting and regularizing buildings of complex urban landscapes. For regularizing, we mean the extraction of the building footprints with precise position and details. In the first step, vegetation points were extracted using a support vector machine (SVM) classifier based on vegetation indexes calculated from color information, then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings. In the second step, we first determined the building boundary points with a modified convex hull algorithm. Then, we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm. Then, two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints. Eventually, the building edges were regularized to form the final building footprints. Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.

通讯作者地址: Gong, JH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Univ Chinese Acad Sci, Beijing, Peoples R China

[ 3 ] Beijing Piesat Informat Technol Co Ltd, Beijing, Peoples R China