[1]
|
Up-Scaling Fuel Hazard Metrics Derived from Terrestrial Laser Scanning Using a Machine Learning Model
Remote Sensing,
2023
DOI:10.3390/rs15051273
|
|
|
[2]
|
Weeds Classification using Convolutional Neural Network Architectures
Journal of Soft Computing Paradigm,
2023
DOI:10.36548/jscp.2023.2.003
|
|
|
[3]
|
Monitoring the Spatial and Interannual Dynamic of Zostera noltei
Wetlands,
2023
DOI:10.1007/s13157-023-01690-7
|
|
|
[4]
|
Improving Forest Detection Using Machine Learning and Remote Sensing: A Case Study in Southeastern Serbia
Applied Sciences,
2023
DOI:10.3390/app13148289
|
|
|
[5]
|
Mixed tropical forests canopy height mapping from spaceborne LiDAR GEDI and multisensor imagery using machine learning models
Remote Sensing Applications: Society and Environment,
2022
DOI:10.1016/j.rsase.2022.100817
|
|
|
[6]
|
Mixed tropical forests canopy height mapping from spaceborne LiDAR GEDI and multisensor imagery using machine learning models
Remote Sensing Applications: Society and Environment,
2022
DOI:10.1016/j.rsase.2022.100817
|
|
|
[7]
|
Stacking of Canopy Spectral Reflectance from Multiple Growth Stages Improves Grain Yield Prediction under Full and Limited Irrigation in Wheat
Remote Sensing,
2022
DOI:10.3390/rs14174318
|
|
|
[8]
|
Using Sentinel-1, Sentinel-2, and Planet satellite data to map field-level tillage practices in smallholder systems
PLOS ONE,
2022
DOI:10.1371/journal.pone.0277425
|
|
|
[9]
|
Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms
Remote Sensing,
2021
DOI:10.3390/rs13101870
|
|
|
[10]
|
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands
Remote Sensing,
2021
DOI:10.3390/rs13183669
|
|
|
[11]
|
Improvement of Wheat Grain Yield Prediction Model Performance Based on Stacking Technique
Applied Sciences,
2021
DOI:10.3390/app112412164
|
|
|
[12]
|
The potential of in-situ hyperspectral remote sensing for differentiating 12 banana genotypes grown in Uganda
ISPRS Journal of Photogrammetry and Remote Sensing,
2020
DOI:10.1016/j.isprsjprs.2020.06.023
|
|
|
[13]
|
The Machine Learning to Detect Drought Risk in Central Java Using Landsat 8 OLI Remote Sensing Images
2019 5th International Conference on Science and Technology (ICST),
2019
DOI:10.1109/ICST47872.2019.9166197
|
|
|
[14]
|
A Random Forest Machine Learning Approach for the Retrieval of Leaf Chlorophyll Content in Wheat
Remote Sensing,
2019
DOI:10.3390/rs11080920
|
|
|
[15]
|
Multi-Temporal Agricultural Land-Cover Mapping Using Single-Year and Multi-Year Models Based on Landsat Imagery and IACS Data
Agronomy,
2019
DOI:10.3390/agronomy9060309
|
|
|
[16]
|
Effect of the Temporal Gradient of Vegetation Indices on Early-Season Wheat Classification Using the Random Forest Classifier
Applied Sciences,
2018
DOI:10.3390/app8081216
|
|
|
[17]
|
Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton
American Journal of Plant Sciences,
2017
DOI:10.4236/ajps.2017.812219
|
|
|
[18]
|
In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest
Remote Sensing,
2017
DOI:10.3390/rs9111184
|
|
|