Leaf and wood separation of poplar seedlings combining locally convex connected patches and K-means++ clustering from terrestrial laser scanning data
Chunhua Hu, Zhou Pan, Tianci Zhong
Abstract
Achieving parameter measurements and three-dimensional visualization for leaves and wood that have been accurately separated from a whole plant is of great significance. We develop a leaf and wood separation method that combines locally convex connected patches (LCCPs) and K-means++ clustering with poplar seedlings as the research object. First, point cloud data of the research samples are collected using terrestrial laser scanning (TLS). Second, an individual poplar seedling point cloud is separated from the complex background using the pass-through filtering algorithm, and noise and outliers are filtered using the statistical filtering algorithm to improve the separation accuracy. Subsequently, the supervoxel segmentation method is introduced to oversegment the poplar seedlings into patches, and the LCCP clustering algorithm is utilized to segment the leaves and branches. The modified K-means++ clustering algorithm is used to further separate connected leaves and branches. To validate the separation method, numerous experiments on normal and water-deficient poplar seedlings are carried out, and the results illustrate that the segmentation accuracy of the leaves are 93.66% and 90.77%, respectively. The results indicate that the proposed method can effectively separate the leaves and wood of poplar seedlings from TLS data.