Impact of Non-Proportional Training Sampling of Imbalanced Classes on Land Cover Classification Accuracy with See5 Decision Tree.
Zhengwei YangClaire G. BoryanPublished in: IGARSS (2019)
Keyphrases
- land cover
- decision trees
- classification accuracy
- training set
- naive bayes
- change detection
- remote sensing
- multispectral
- satellite images
- remote sensing images
- remote sensing data
- land cover classification
- remotely sensed images
- supervised classification
- support vector
- training data
- environmental variables
- geographic information systems
- remotely sensed data
- urban areas
- neural network
- urban growth
- databases