Predicting Canopy Chlorophyll Content in Sugarcane Crops Using Machine Learning Algorithms and Spectral Vegetation Indices Derived from UAV Multispectral Imagery.
Amarasingam NarmilanFelipe GonzalezArachchige Surantha Ashan SalgadoeUnupen Widanelage Lahiru Madhushanka KumarasiriHettiarachchige Asiri Sampageeth WeerasingheBuddhika Rasanjana KulasekaraPublished in: Remote. Sens. (2022)
Keyphrases
- machine learning algorithms
- multispectral
- hyperspectral
- remote sensing
- remotely sensed
- remote sensing data
- spectral data
- hyperspectral data
- hyperspectral images
- multispectral images
- land cover
- satellite imagery
- spectral images
- hyperspectral imagery
- remote sensing images
- benchmark data sets
- satellite images
- spectral bands
- machine learning
- high resolution
- image data
- spectral characteristics
- remotely sensed data
- learning algorithm
- multi spectral images
- multi band
- machine learning methods
- spectral resolution
- land cover classification
- change detection
- image analysis
- decision trees
- spatial resolution
- remote sensing imagery
- target detection
- digital elevation models
- image processing
- satellite data
- multispectral imaging
- machine learning approaches
- statistical machine learning
- normalized difference vegetation index
- high spatial resolution
- remotely sensed images
- machine learning models
- image fusion
- information content
- infrared
- multispectral satellite images
- standard machine learning algorithms
- computer vision
- pattern recognition
- data sets
- neural network
- high quality
- data streams
- domain specific
- urban areas
- visible spectrum