Individual Rubber Tree Segmentation Based on Ground-Based LiDAR Data and Faster R-CNN of Deep Learning

• Published in: Forests Vol. 10; no. 9; p. 793
• Main Authors: Wang, Jiamin, Chen, Xinxin, Cao, Lin, An, Feng, Chen, Bangqian, Xue, Lianfeng, Yun, Ting
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 11.09.2019
• Subjects: Algorithms; Artificial neural networks; China; clones; Cloning; Data collection; Deep learning; Hevea brasiliensis; Hurricanes; Image segmentation; Inspection; Lasers; leaves; Lidar; Machine learning; Morphology; Natural disturbance; Neural networks; Object recognition; Rubber; Rubber trees; Training; tree crown; tree trunk; Trees; Typhoons; Wavelet transforms; China; Hainan Island
• DOI: 10.3390/f10090793
• ISSN: 1999-4907

Rubber trees in southern China are often impacted by natural disturbances that can result in a tilted tree body. Accurate crown segmentation for individual rubber trees from scanned point clouds is an essential prerequisite for accurate tree parameter retrieval. In this paper, three plots of different rubber tree clones, PR107, CATAS 7-20-59, and CATAS 8-7-9, were taken as the study subjects. Through data collection using ground-based mobile light detection and ranging (LiDAR), a voxelisation method based on the scanned tree trunk data was proposed, and deep images (i.e., images normally used for deep learning) were generated through frontal and lateral projection transform of point clouds in each voxel with a length of 8 m and a width of 3 m. These images provided the training and testing samples for the faster region-based convolutional neural network (Faster R-CNN) of deep learning. Consequently, the Faster R-CNN combined with the generated training samples comprising 802 deep images with pre-marked trunk locations was trained to automatically recognize the trunk locations in the testing samples, which comprised 359 deep images. Finally, the point clouds for the lower parts of each trunk were extracted through back-projection transform from the recognized trunk locations in the testing samples and used as the seed points for the region’s growing algorithm to accomplish individual rubber tree crown segmentation. Compared with the visual inspection results, the recognition rate of our method reached 100% for the deep images of the testing samples when the images contained one or two trunks or the trunk information was slightly occluded by leaves. For the complicated cases, i.e., multiple trunks or overlapping trunks in one deep image or a trunk appearing in two adjacent deep images, the recognition accuracy of our method was greater than 90%. Our work represents a new method that combines a deep learning framework with point cloud processing for individual rubber tree crown segmentation based on ground-based mobile LiDAR scanned data.