Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and PolSAR Data: A Comparative Evaluation
In recent years, several powerful machine learning (ML) algorithms have been developed for image classification, especially those based on ensemble learning (EL). In particular, Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) methods have attracted researchers’ att...
Enregistré dans:
Auteurs principaux: | Hamid Jafarzadeh, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, Saeid Homayouni |
---|---|
Format: | article |
Langue: | EN |
Publié: |
MDPI AG
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/be0aeffa2aae4a19b80dce589367d7a7 |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
A New Convolutional Kernel Classifier for Hyperspectral Image Classification
par: Mohsen Ansari, et autres
Publié: (2021) -
Blind Fusion of Hyperspectral Multispectral Images Based on Matrix Factorization
par: Jian Long, et autres
Publié: (2021) -
Monitoring and Analyzing Yield Gap in Africa through Soil Attribute Best Management Using Remote Sensing Approaches: A Review
par: Keltoum Khechba, et autres
Publié: (2021) -
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud
par: Itiya Aneece, et autres
Publié: (2021) -
A multispectral 3D-Endoscope for Cholesteatoma Removal
par: Wisotzky Eric L., et autres
Publié: (2020)