UAV4TREE: DEEP LEARNING-BASED SYSTEM FOR AUTOMATIC CLASSIFICATION OF TREE SPECIES USING RGB OPTICAL IMAGES OBTAINED BY AN UNMANNED AERIAL VEHICLE

• Published in: ISPRS annals of the photogrammetry, remote sensing and spatial information sciences remote sensing and spatial information sciences, 2023-01, Vol.X-1/W1-2023, p.1089-1096
• Main Authors: Pierdicca, R., Nepi, L., Mancini, A., Malinverni, E. S., Balestra, M.
• Format: Article
• Language: English
• Subjects: Automation; Classification; Data augmentation; Datasets; Deep learning; Forest management; Generative adversarial networks; Image classification; Machine learning; Plant species; Sustainability management; Sustainable forestry; Unmanned aerial vehicles
• ISSN: 2194-9050; 2194-9042; 2194-9050
• DOI: 10.5194/isprs-annals-X-1-W1-2023-1089-2023

Automated tree classification from unmanned aerial vehicle (UAV) images is a challenging task with several applications in forest management and conservation. In this study, we propose UAV4Tree a Deep Learning based system that automatically classifies RGB optical images obtained by the UAV. In particular, we explore the use of augmented datasets and various deep learning models, including ResNet, DenseNet, InceptionV3, and Vision Transformer, for the classification of tree images obtained from UAVs. Our experiments show that the use of an augmented dataset can significantly improve the accuracy of the classification by approximately 10 points compared to the use of a non-augmented dataset. We also found that fine-tuning and the introduction of dropout were essential for improving the generalization ability of the models on the augmented dataset. Furthermore, the use of Super Resolution Generative Adversarial Network (SR-GAN) in the original dataset allowed us to increase the performance of some models. Our findings provide valuable insights into the use of deep learning models for automated tree classification from UAV imagery, which has significant implications for sustainable forest management and conservation.