Classification of Tree Species Based on Point Cloud Projection Images with Depth Information

Abstract

To address the disorderliness issue of point cloud data when directly used for tree species classification, this study transformed point cloud data into projected images for classification. Building upon this foundation, the influence of incorporating multiple distinct projection perspectives, integrating depth information, and utilising various classification models on the classification of tree point cloud projected images was investigated. Nine tree species in Sanjiangkou Ecological Park, Fuzhou City, were selected as samples. In the single-direction projection classification, the X-direction projection exhibited the highest average accuracy of 80.56%. In the dual-direction projection classification, the XY-direction projection exhibited the highest accuracy of 84.76%, which increased to 87.14% after adding depth information. Four classification models (convolutional neural network, CNN; visual geometry group, VGG; ResNet; and densely connected convolutional networks, DenseNet) were used to classify the datasets, with average accuracies of 73.53%, 85.83%, 87%, and 86.79%, respectively. Utilising datasets with depth and multidirectional information can enhance the accuracy and robustness of image classification. Among the models, the CNN served as a baseline model, VGG accuracy was 12.3% higher than that of CNN, DenseNet had a smaller gap between the average accuracy and the optimal result, and ResNet performed the best in classification tasks.