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Segmentation and Clustering of 3D Forest Point Cloud Using Mean Shift Algorithms
2016
Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
unpublished
Preserved Fulltext
Segmenting individual trees from the forest point cloud has significant implications in forestry inventory. This paper presents a novel computational scheme to segment and cluster the 3D point cloud data acquired by an airborne LiDAR. The scheme employs a mean shift-based iterative procedure on the data sets in a defined complex multimodal feature space to cluster points with similar modes together. Experimental results reveal that the proposed scheme can work effectively and the average
doi:10.2991/icmmct-16.2016.250
fatcat:4fq64ubehzg7dhmj57itg4eiri