This paper presents an automatic algorithm to localize and extract urban trees from mobile LiDAR point clouds. First, in order to reduce the number of points to be processed, the ground points are filtered out from the raw point clouds, and the un-ground points are segmented into supervoxels. Then, a novel localization method is proposed to locate the urban trees accurately. Next, a segmentation method by localization is proposed to achieve objects. Finally, the features of objects are extracted, and the feature vectors are classified by random forests trained on manually labeled objects. The proposed method has been tested on a point cloud dataset. The results prove that our algorithm efficiently extracts the urban trees.

Date Published: 2 March 2016
PDF: 6 pages
Proc. SPIE 9901, 2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015), 99010P (2 March 2016); doi: 10.1117/12.2234795

Published in SPIE Proceedings Vol. 9901:
2nd ISPRS International Conference on Computer Vision in Remote Sensing (CVRS 2015)
Cheng Wang; Rongrong Ji; Chenglu Wen, Editor(s)