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...
Saved in:
Main Authors: | Hamid Jafarzadeh, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, Saeid Homayouni |
---|---|
Format: | article |
Language: | EN |
Published: |
MDPI AG
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/be0aeffa2aae4a19b80dce589367d7a7 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A New Convolutional Kernel Classifier for Hyperspectral Image Classification
by: Mohsen Ansari, et al.
Published: (2021) -
Blind Fusion of Hyperspectral Multispectral Images Based on Matrix Factorization
by: Jian Long, et al.
Published: (2021) -
Monitoring and Analyzing Yield Gap in Africa through Soil Attribute Best Management Using Remote Sensing Approaches: A Review
by: Keltoum Khechba, et al.
Published: (2021) -
Classifying Crop Types Using Two Generations of Hyperspectral Sensors (Hyperion and DESIS) with Machine Learning on the Cloud
by: Itiya Aneece, et al.
Published: (2021) -
A multispectral 3D-Endoscope for Cholesteatoma Removal
by: Wisotzky Eric L., et al.
Published: (2020)