Segmentation of Individual Trees in TLS Point Clouds via Graph Optimization

Abstract

Individual tree segmentation from terrestrial laser scanning (TLS) point clouds is essential for precise forest inventory, instance-level tree modeling, and the estimation of forest stock volume. However, current instance-level segmentation techniques encounter significant challenges in complex forest environments, particularly those characterized by dense understory vegetation and substantial crown overlap in natural forests. These complexities reduce segmentation accuracy and limit the generalizability of existing methods across diverse forest types. This article presents a unified method for individual tree segmentation that integrates trunk localization with crown segmentation. The trunk localization uses normal vector features to eliminate nontrunk slice points, employs an enhanced density-based spatial clustering of applications with noise (DBSCAN) algorithm for trunk slice separation, and refines trunk positions by fitting circular-like trunk slices using the Hough transform (HT). This integrated approach ensures precise segmentation and optimization of final trunk positions. Subsequently, a graph-based optimization method is applied for crown segmentation. This method incorporates supervoxel technology, an optimal Euclidean distance metric between supervoxels, and a supervoxel similarity metric to construct an optimal undirected graph. Tree crown supervoxels are segmented by tracing the shortest path from the crown supervoxels to their corresponding tree roots. We validated the proposed method on eight sample plots representing various complexities and forest types. For tree trunk localization, the proposed method achieved an average Mean_accuracy of 0.761, which is 27% higher than the best result among the three traditional methods. For crown segmentation, it achieved an average mean intersection over union (mIoU) of 0.645, marking a 31% improvement over the best baseline performance.