A New Individual Tree Species Classification Method Based on the ResU-Net Model

Caiyan Chen,Yunwei Tang,Hui Li,Linhai Jing

doi:10.3390/f12091202

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.

Highlights

Forest resources are among the most important natural resources for humankind, and increasing attention is being paid to the management of forest resources [1,2,3]
Remote sensing imagery and field sampling data in the study area were used to construct and enhance a remote sensing imagery individual tree species (ITS) sample set, and a ResU-Net model was proposed for ITS classification
By comparing the classification results of the improved U-Net model with those of the two ResU-Net models, we found that the training and verification accuracies of the three models were all above 93%, and the overall accuracies were all greater than 92%

Summary

Introduction

Forest resources are among the most important natural resources for humankind, and increasing attention is being paid to the management of forest resources [1,2,3]. ITS classification technology is mainly based on high-spatial-resolution airborne multispectral or hyperspectral data, high-point-density LiDAR point cloud data, or their combination [11,12,13]. With these data, a variety of ITSs can be identified, and high classification accuracies can be obtained [14,15,16]. Guan et al [17] used deep Boltzmann machines (DBMs) and LiDAR data to obtain the high-level features of individual trees; they used a support vector machine (SVM) to classify ten tree species, with an overall accuracy of 86.1%. Wang et al [19] selected unmanned aerial vehicle (UAV) imagery and a back propagation (BP) neural network to classify six tree species, with an overall accuracy of 89.1%

Methods

Results

Discussion

Conclusion