DEAD WOOD DETECTION BASED ON SEMANTIC SEGMENTATION OF VHR AERIAL CIR IMAGERY USING OPTIMIZED FCN-DENSENET

10 citations as recorded by crossref.

1. Deep Learning-Based Automated Forest Health Diagnosis From Aerial Images C. Chiang et al. 10.1109/ACCESS.2020.3012417
2. Review on Convolutional Neural Networks (CNN) in vegetation remote sensing T. Kattenborn et al. 10.1016/j.isprsjprs.2020.12.010
3. Densely multiscale framework for segmentation of high resolution remote sensing imagery I. Bello et al. 10.1016/j.cageo.2022.105196
4. Automatic resin duct detection and measurement from wood core images using convolutional neural networks A. Fabijańska & G. Cahalan 10.1038/s41598-023-34304-7
5. Detection of Standing Deadwood from Aerial Imagery Products: Two Methods for Addressing the Bare Ground Misclassification Issue K. Zielewska-Büttner et al. 10.3390/f11080801
6. Deep learning for forest inventory and planning: a critical review on the remote sensing approaches so far and prospects for further applications A. Hamedianfar et al. 10.1093/forestry/cpac002
7. Detection of Trees on Street-View Images Using a Convolutional Neural Network D. Jodas et al. 10.1142/S0129065721500428
8. Review on the Possibilities of Mapping Old-Growth Temperate Forests by Remote Sensing in Europe M. Hirschmugl et al. 10.1007/s10666-023-09897-y
9. Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review S. Marvasti-Zadeh et al. 10.1145/3625387
10. Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images S. Li et al. 10.3390/rs16010031