Donglin Fan

Orcid: 0000-0002-2100-6634

According to our database1, Donglin Fan authored at least 14 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Comparison of 2D and 3D vegetation species mapping in three natural scenarios using UAV-LiDAR point clouds and improved deep learning methods.
Int. J. Appl. Earth Obs. Geoinformation, December, 2023

Quantifying scattering characteristics of mangrove species from Optuna-based optimal machine learning classification using multi-scale feature selection and SAR image time series.
Int. J. Appl. Earth Obs. Geoinformation, August, 2023

Classification of Citrus Huanglongbing Degree Based on CBAM-MobileNetV2 and Transfer Learning.
Sensors, 2023

Large-Scale Oceanic Dynamic Field Visualization Based on WebGL.
IEEE Access, 2023

2022
Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data.
Remote. Sens., December, 2022

Consistency Assessments of the Land Cover Products on the Tibetan Plateau.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Combination of Hyperspectral and Quad-Polarization SAR Images to Classify Marsh Vegetation Using Stacking Ensemble Learning Algorithm.
Remote. Sens., 2022

Comparison of Different Transfer Learning Methods for Classification of Mangrove Communities Using MCCUNet and UAV Multispectral Images.
Remote. Sens., 2022

Evaluation of Decision Fusions for Classifying Karst Wetland Vegetation Using One-Class and Multi-Class CNN Models with High-Resolution UAV Images.
Remote. Sens., 2022

Comparison of RFE-DL and stacking ensemble learning algorithms for classifying mangrove species on UAV multispectral images.
Int. J. Appl. Earth Obs. Geoinformation, 2022

2021
An Effective Method for Canopy Chlorophyll Content Estimation of Marsh Vegetation Based on Multiscale Remote Sensing Data.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2021

Study on transfer learning ability for classifying marsh vegetation with multi-sensor images using DeepLabV3+ and HRNet deep learning algorithms.
Int. J. Appl. Earth Obs. Geoinformation, 2021

Comparison of optimized object-based RF-DT algorithm and SegNet algorithm for classifying Karst wetland vegetation communities using ultra-high spatial resolution UAV data.
Int. J. Appl. Earth Obs. Geoinformation, 2021

2020
An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data.
Remote. Sens., 2020


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