Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory.
Tedros M. BerhaneCharles R. LaneQiusheng WuBradley C. AutreyOleg A. AnenkhonovVictor V. ChepinogaHongxing LiuPublished in: Remote. Sens. (2018)
Keyphrases
- random forest
- decision trees
- multispectral
- high resolution
- remote sensing
- hyperspectral data
- satellite images
- random forests
- remote sensing data
- image data
- feature set
- fold cross validation
- high spatial resolution
- spatial resolution
- remote sensing images
- decision tree learning algorithms
- land cover classification
- hyperspectral
- multispectral images
- hyperspectral images
- machine learning algorithms
- naive bayes
- classification models
- classification accuracy
- training data
- image analysis
- machine learning
- ensemble methods
- image processing
- change detection
- spectral images
- land cover
- hyperspectral imagery
- super resolution
- feature selection
- ensemble classifier
- logistic regression
- image fusion
- support vector machine
- multi label
- decision tree learning algorithm
- spectral bands
- machine learning methods
- base classifiers
- image classification
- feature space
- support vector
- high quality
- computer vision
- information fusion
- prediction accuracy
- training set
- pattern recognition