Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional Neural Network) and by Post-classification Comparison (PCC).
Guanyao XieSimona NiculescuPublished in: Remote. Sens. (2021)
Keyphrases
- machine learning algorithms
- random forest
- land cover
- decision trees
- random forests
- support vector machine
- benchmark data sets
- machine learning
- convolutional neural network
- machine learning methods
- land cover classification
- remote sensing
- learning algorithm
- multispectral
- remote sensing images
- supervised classification
- satellite images
- change detection
- ensemble methods
- ensemble classifier
- feature set
- training data
- feature selection
- training set
- geographic information systems
- svm classifier
- pattern recognition
- support vector
- classification algorithm
- naive bayes
- support vector machine svm
- classification models
- urban growth
- logistic regression
- feature vectors
- classification accuracy
- multi class
- base classifiers
- feature extraction
- supervised learning
- object detection
- text classification
- unsupervised learning
- data sets
- image processing