Abstract
There are normally three main steps to carrying out the labeling of airborne laser scanning (ALS) point clouds. The first step is to use appropriate primitives to represent the scanning scenes, the second is to calculate the discriminative features of each primitive, and the third is to introduce a classifier to label the point clouds. This paper investigates multiple primitives to effectively represent scenes and exploit their geometric relationships. Relationships are graded according to the properties of related primitives. Then, based on initial labeling results, a novel, hierarchical, and optimal strategy is developed to optimize semantic labeling results. The proposed approach was tested using two sets of representative ALS point clouds, namely the Vaihingen datasets and Hong Kong’s Central District dataset. The results were compared with those generated by other typical methods in previous work. Quantitative assessments for the two experimental datasets showed that the performance of the proposed approach was superior to reference methods in both datasets. The scores for correctness attained over 98% in all cases of the Vaihingen datasets and up to 96% in the Hong Kong dataset. The results reveal that our approach of labeling different classes in terms of ALS point clouds is robust and bears significance for future applications, such as 3D modeling and change detection from point clouds.
Highlights
There are three common geometric primitives that have been used in classification issues for the past decade: points, planar segments, and voxels
We focus on the supervised strategies of airborne laser scanning (ALS) point clouds
As we described in the introduction, the authors used planar segments, points, and mean shift segments to carry out three independent classifications
Summary
There are three common geometric primitives that have been used in classification issues for the past decade: points, planar segments, and voxels. Point clouds are first classified using planar segments, after which, the points are assigned different labels Those points labeled “vegetation” or “unclassified” are used to carry out the second step, namely point-based contextual classification. Only the roof elements limit the effects of mean shift segments Taking these issues into account, we present a novel framework in the segmentation step, i.e., using multiple primitives to represent scenes. The main contributions of this paper can be described : (1) multiple primitives are introduced to represent scenes in the segmentation step; (2) the relationships between primitives are categorized into two grades; and (3) an innovative hierarchical strategy is proposed to realize labeling optimization.
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