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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 accuracy of tree detection (88.6%) can meet the requirements of forest inventory.

Segmentation and Clustering of 3D Forest Point Cloud Using Mean Shift Algorithms