Abstract
Diverse approaches to laser point segmentation have been proposed since the emergence of the laser scanning system. Most of these segmentation techniques, however, suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance. In an effort to overcome these drawbacks, this paper proposes a segmentation methodology that: (1) reduces the dimensions of the attribute space; (2) considers the attribute similarity and the proximity of the laser point simultaneously; and (3) works well with both airborne and terrestrial laser scanning data. A neighborhood definition based on the shape of the surface increases the homogeneity of the laser point attributes. The magnitude of the normal position vector is used as an attribute for reducing the dimension of the accumulator array. The experimental results demonstrate, through both qualitative and quantitative evaluations, the outcomes’ high level of reliability. The proposed segmentation algorithm provided 96.89% overall correctness, 95.84% completeness, a 0.25 m overall mean value of centroid difference, and less than 1° of angle difference. The performance of the proposed approach was also verified with a large dataset and compared with other approaches. Additionally, the evaluation of the sensitivity of the thresholds was carried out. In summary, this paper proposes a robust and efficient segmentation methodology for abstraction of an enormous number of laser points into plane information.
Highlights
There has been significant interest in developing segmentation methodologies useful for the various applications such as Digital Building Model creation, large-scale ortho-photo generation, city modeling, object recognition, and photorealistic 3D modeling
The specific objectives of the present research are to propose a segmentation approach that (1) considers both the similarity and the proximity of laser points simultaneously; this will remove the problems from the above-mentioned approaches; (2) reduces the dimensions of the attribute space for efficiency or memory requirement purposes; and (3) works well with both airborne and terrestrial laser scanner point clouds
Prior segmentation techniques suffer from limitations such as sensitivity to the choice of seed points, lack of consideration of the spatial relationships among points, and inefficient performance
Summary
There has been significant interest in developing segmentation methodologies useful for the various applications such as Digital Building Model creation, large-scale ortho-photo generation, city modeling, object recognition, and photorealistic 3D modeling. The first category includes the methodologies that segment point clouds based on criteria such as point proximity and attribute similarity, both locally estimated from the surface. Scan line segmentation [2,3], and the surface growing method [4] belong to this first category. The scan line segmentation method first splits each scan line into straight line segments and based on a similarity criterion, merges them with adjacent scan line segments. The split-and-merge approach to image segmentation is applied to 3D space. The surface growing method extends seed regions to adjacent points by considering the proximity and similarity of points [4,5,6,7,8]
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