Multi-branch and multi-label tree species classification using deep learning for UAV aerial photography and Sentinel remote sensing images.

The classification and identification of forest tree species is of great value in the study of species diversity and forest monitoring. With the development of emerging technologies, the combination of remote sensing images and deep learning methods has become an important means to study multi-label image classification. However, nowadays, due to the small difference between tree species images, the difficulty of artificial labeling, and the difficulty of obtaining data sets, there are few studies on multi-label classification for tree species images. Therefore, taking the TreeSatAI dataset as an example, a multi-branch and multi-label image classification model (MMTSC) specifically designed for multi-source remote sensing data is proposed to classify and identify 15 tree species in the dataset. In a complex forest stand scenario with unbalanced data, our F1-Score and Precision are as high as about 72% and 82%, respectively. The visualization results of the confusion matrix and Grad-CAM heat map further verify the model's recognition ability on different categories. To comprehensively evaluate the model performance, we compared it with other state-of-the-art (SOTA) methods for multi-label image classification tasks and conducted a series of ablation experiments. Experimental results show that the MMTSC model outperforms other SOTA methods in F1-Score, Precision, Recall, and mAP. In addition, we also compared the model's backbone network DenseNet121 with the classic structures of EfficientNet-B0, ConvNeXt-Tiny, ResNet-18, MobileNetV3 and RegNetX-800MF. The evaluation results showed that the DenseNet121 architecture performed best in this task, verifying its effectiveness and adaptability as a backbone network. Finally, we use the results of the deep learning-based multi-label tree species classification model for biomass estimation, providing practical suggestions for relevant institutions, thereby contributing to the scientific management of forest resources and the improvement of carbon sequestration capacity.