A Three-Step Approach for TLS Point Cloud Classification

doi:10.1109/TGRS.2016.2564501

A Three-Step Approach for TLS Point Cloud Classification

2016-09-01

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Z

Zhuqiang Li

张

张立强 (Liqiang Zhang) *

P. Takis Mathiopoulos

DOI：10.1109/TGRS.2016.2564501

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The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computedon three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

Keywords:

Discriminative feature

multilabel graph-cut

object-oriented decision tree

optimization

point cloud

A Three-Step Approach for TLS Point Cloud Classification

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