RESNET-BASED TREE SPECIES CLASSIFICATION USING UAV IMAGES

42 citations as recorded by crossref.

1. Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning R. Minařík et al. 10.3390/rs13234768
2. An automated, high-performance approach for detecting and characterizing broccoli based on UAV remote-sensing and transformers: A case study from Haining, China C. Zhou et al. 10.1016/j.jag.2022.103055
3. Tree Species Classification in Subtropical Natural Forests Using High-Resolution UAV RGB and SuperView-1 Multispectral Imageries Based on Deep Learning Network Approaches: A Case Study within the Baima Snow Mountain National Nature Reserve, China X. Chen et al. 10.3390/rs15102697
4. Tree Species Classification from UAV Canopy Images with Deep Learning Models Y. Huang et al. 10.3390/rs16203836
5. Consumer-grade UAV imagery facilitates semantic segmentation of species-rich savanna tree layers M. Popp & J. Kalwij 10.1038/s41598-023-40989-7
6. Detection of Invasive Species in Wetlands: Practical DL with Heavily Imbalanced Data M. Cabezas et al. 10.3390/rs12203431
7. UAV in the advent of the twenties: Where we stand and what is next F. Nex et al. 10.1016/j.isprsjprs.2021.12.006
8. UAV-g 2019: Unmanned Aerial Vehicles in Geomatics F. Nex 10.3390/drones3030074
9. Pine wilt disease detection in high-resolution UAV images using object-oriented classification Z. Sun et al. 10.1007/s11676-021-01420-x
10. Individual tree canopy detection and species classification of conifers by deep learning Y. Hayashi et al. 10.20659/jjfp.55.1_3
11. A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods L. Zhong et al. 10.3390/f15050852
12. Estimating Carbon Stock in Unmanaged Forests Using Field Data and Remote Sensing T. Leditznig & H. Klug 10.3390/rs16213926
13. Developing landscape-scale forest restoration targets that embrace spatial pattern M. Rudge et al. 10.1007/s10980-022-01461-5
14. Identification of Brush Species and Herbicide Effect Assessment in Southern Texas Using an Unoccupied Aerial System (UAS) X. Shen et al. 10.3390/rs15133211
15. Efficient Classification of Remote Sensing Images Using Two Convolution Channels and SVM K. A. AlAfandy et al. 10.32604/cmc.2022.022457
16. Deep learning based high performance classification architecture for low-altitude aerial images P. Mittal et al. 10.1007/s11042-023-16195-y
17. Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning S. Kentsch et al. 10.3390/s21020471
18. 2D tactile sensor based on multimode interference and deep learning Z. Ding & Z. Zhang 10.1016/j.optlastec.2020.106760
19. Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks F. Schiefer et al. 10.1016/j.isprsjprs.2020.10.015
20. Explainable identification and mapping of trees using UAV RGB image and deep learning M. Onishi & T. Ise 10.1038/s41598-020-79653-9
21. Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest J. Anwander et al. 10.3390/rs16030561
22. Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art C. Kwenda et al. 10.1109/ACCESS.2022.3170049
23. Automated mapping of Portulacaria afra canopies for restoration monitoring with convolutional neural networks and heterogeneous unmanned aerial vehicle imagery N. Galuszynski et al. 10.7717/peerj.14219
24. Tree Species Classification by Multi-Season Collected UAV Imagery in a Mixed Cool-Temperate Mountain Forest R. Avtar et al. 10.3390/rs16214060
25. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. 10.1016/j.isprsjprs.2020.12.010
26. Practicality and Robustness of Tree Species Identification Using UAV RGB Image and Deep Learning in Temperate Forest in Japan M. Onishi et al. 10.3390/rs14071710
27. Tree species classification on images from airborne mobile mapping using ML.NET M. Michałowska et al. 10.1080/22797254.2023.2271651
28. Detection of Pine Wilt Disease Using Drone Remote Sensing Imagery and Improved YOLOv8 Algorithm: A Case Study in Weihai, China S. Wang et al. 10.3390/f14102052
29. Establishment of a Tool for Determining the Female Strobili Developmental Stage of Japanese Black Pine (<i>Pinus thunbergii</i>) Using Convolutional Neural Network MobileNetV2 N. Miyamoto et al. 10.4005/jjfs.105.316
30. Towards operational UAV-based forest health monitoring: Species identification and crown condition assessment by means of deep learning S. Ecke et al. 10.1016/j.compag.2024.108785
31. TSCMDL: Multimodal Deep Learning Framework for Classifying Tree Species Using Fusion of 2-D and 3-D Features B. Liu et al. 10.1109/TGRS.2023.3266057
32. A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm P. Jayagopal et al. 10.1038/s41598-024-54399-w
33. Classification of Tree Species Based on Point Cloud Projection Images with Depth Information Z. Fan et al. 10.3390/f14102014
34. Individual tree species identification using Dense Convolutional Network (DenseNet) on multitemporal RGB images from UAV S. Natesan et al. 10.1139/juvs-2020-0014
35. Potential of Lightweight Drones and Object-Oriented Image Segmentation in Forest Plantation Assessment J. Dixit et al. 10.3390/rs16091554
36. A systematic review of machine learning and deep learning approaches in plant species detection D. Barhate et al. 10.1016/j.atech.2024.100605
37. A Novel Deep Learning Method to Identify Single Tree Species in UAV-Based Hyperspectral Images G. Miyoshi et al. 10.3390/rs12081294
38. Investment of Classic Deep CNNs and SVM for Classifying Remote Sensing Images K. AlAfandy et al. 10.25046/aj050580
39. Individual Tree-Crown Detection and Species Identification in Heterogeneous Forests Using Aerial RGB Imagery and Deep Learning M. Beloiu et al. 10.3390/rs15051463
40. UAV-Based Detection of Deciduous Tree Species Using Structural and Spectral Characteristics M. Naseri & S. Shataee Jouibary 10.1007/s12524-024-01944-9
41. Tree Species Identification in Urban Environments Using TensorFlow Lite and a Transfer Learning Approach D. Pacheco-Prado et al. 10.3390/f14051050
42. Detection of Aquatic Invasive Plants in Wetlands of the Upper Mississippi River from UAV Imagery Using Transfer Learning G. Chaudhuri & N. Mishra 10.3390/rs15030734