Evaluating the Effect of Training Data Size and Composition on the Accuracy of Smallholder Irrigated Agriculture Mapping in Mozambique Using Remote Sensing and Machine Learning Algorithms.
Timon WeitkampPoolad KarimiPublished in: Remote. Sens. (2023)
Keyphrases
- machine learning algorithms
- remote sensing
- learning algorithm
- remotely sensed
- predictive accuracy
- decision trees
- training data
- benchmark data sets
- change detection
- remote sensing images
- multispectral
- machine learning
- hyperspectral
- classification accuracy
- machine learning methods
- image processing
- high resolution
- remote sensing imagery
- remote sensing data
- learning problems
- satellite images
- satellite data
- decision tree learners
- land cover
- image analysis
- automatic image registration
- image fusion
- data sets
- machine learning models
- supervised learning
- machine learning approaches
- digital image analysis
- hyperspectral remote sensing
- high spatial resolution
- information gain
- satellite imagery
- multi spectral images
- hyperspectral imagery
- geographical information systems
- naive bayes
- standard machine learning algorithms
- training set
- infrared
- hyperspectral images
- remote sensed images
- spectral resolution
- remotely sensed images
- image data
- high quality
- neural network