Estimating fractional vegetation cover of maize under water stress from UAV multispectral imagery using machine learning algorithms.
Yaxiao NiuWenting HanHuihui ZhangLiyuan ZhangHaipeng ChenPublished in: Comput. Electron. Agric. (2021)
Keyphrases
- machine learning algorithms
- multispectral
- remote sensing
- remote sensing data
- remotely sensed
- digital elevation models
- land cover
- satellite imagery
- satellite images
- image data
- remotely sensed data
- remote sensing images
- high resolution
- benchmark data sets
- machine learning
- learning algorithm
- decision trees
- spatial resolution
- hyperspectral
- machine learning methods
- multispectral images
- image analysis
- remotely sensed images
- learning problems
- multi band
- hyperspectral data
- multispectral satellite images
- satellite data
- high spatial resolution
- machine learning models
- random forests
- change detection
- image processing
- spectral characteristics
- hyperspectral imagery
- machine learning approaches
- learning models
- spectral bands
- spectral images
- land cover classification
- pattern recognition
- data streams
- active learning
- data mining
- image fusion
- statistical machine learning
- multispectral imaging
- supervised learning
- standard machine learning algorithms
- normalized difference vegetation index