A New Individual Tree Species Classification Method Based on the ResU-Net Model
Caiyan Chen, Linhai Jing, Hui Li, Yunwei Tang
Abstract
Individual tree species (ITS) classification is one of the key issues in forest resource management. Compared with traditional classification methods, deep learning networks may yield ITS classification results with higher accuracy. In this research, the U-Net and ResNet networks were combined to form a Res-UNet network by changing the structure of the convolutional layer to the residual structure in ResNet based on the framework of the U-Net model. In addition, a second Res-UNet network named Res-UNet2 was further constructed to explore the effect of the stacking of residual structures on network performance. The Res-UNet2 model structure is similar to that of the Res-UNet model, but the convolutional layer in the U-Net model is created with a double-layer residual structure. The two networks proposed in this work were used to classify ITSs in WorldView-3 images of the Huangshan Mountains, Anhui Province, China, acquired in March 2019. The resulting ITS map was compared with the classification results obtained with U-Net and ResNet. The total classification accuracy of the ResU-Net network reached 94.29% and was higher than that generated by the U-Net and ResNet models, verifying that the ResU-Net model can more accurately classify ITSs. The Res-UNet2 model performed poorly compared to Res-UNet, indicating that stacking the residual modules in ResNet does not achieve an accuracy improvement.