Ms. Ge Wang Profile

Ge Wang

at Wuhan Univ.

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Publications (2)

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Proceedings Article | 5 November 2020 Paper

Segmentation of street trees from MLS point clouds by dimensional feature analysis and improved FCM algorithm

Jingzhong Xu, Ge Wang

Proceedings Volume 11562, 1156207 (2020) https://doi.org/10.1117/12.2574739

KEYWORDS: Clouds, LIDAR, Laser applications, Algorithm development, Principal component analysis

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Street trees are important elements of urban ecological environment. Due to the complex shape of trees, there is still no effective means to identify trees from Mobile Laser Scanning (MLS) point clouds. Aims to segment single street tree from MLS point clouds, this study proposed a new method of extracting street trees by dimensional feature analysis and improved FCM method. After filtering MLS point clouds, dimensional features are introduced to detect candidate tree trunk and tree canopy points. Then, region growing method and cross-validation method are used to extract street tree points according to tree semantic rules. Thirdly, tree positions are taken as initial values for the FCM algorithm to cluster tree points, which also are used to limit the change of the cluster center during cluster process to improve the segmentation effect of trees with overlapping canopies. To evaluate the performance of the method, datasets with different size street trees are tested in the experiment and results show that the proposed method can segment single street trees from MLS point clouds effectively.

Proceedings Article | 7 March 2019 Paper

Extracting road edges from MLS point clouds via a local planar fitting algorithm

Jingzhong Xu, Ge Wang, Lina Ma, Jiarong Wang

Proceedings Volume 11053, 110531O (2019) https://doi.org/10.1117/12.2511423

KEYWORDS: Roads, Clouds, LIDAR, Laser scanners

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As the basic element of a road, road edges are of great significance for intelligent transportation and urban foundational geographic information construction. Mobile laser scanning (MLS) provides an effective way to extract road information, but it is difficult to extract accurate road edges from a large-scale dataset with complex road conditions. In this paper, we propose a method to extract road edges from MLS data based on a local planar fitting algorithm. First, scanning lines are extracted based on the horizontal projection distance between the laser points. Second, a planar fitting method is adopted to extract road curb points. Road curb points are then clustered and optimized by differentiating the distance between road curb points and the auxiliary line. Finally, a linear least squares fitting method is applied to obtain the road edges. Three experimental datasets with multi-type road markings were used to evaluate the performance of the proposed method. The results demonstrate the feasibility and effectiveness of the proposed method.

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