Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture.
Manal ElarabAndres M. TiclavilcaAlfonso F. Torres-RuaInga MaslovaMac McKeePublished in: Int. J. Appl. Earth Obs. Geoinformation (2015)
Keyphrases
- feature vectors
- multispectral
- satellite images
- high resolution
- relevance vector machines
- remote sensing
- precision agriculture
- satellite imagery
- visible spectrum
- digital elevation models
- remote sensing data
- image data
- low resolution
- spectral data
- high spatial resolution
- remote sensing images
- lidar data
- change detection
- spatial resolution
- multispectral images
- hyperspectral
- data mining
- super resolution
- multi band
- image fusion
- multispectral satellite images
- face images
- land cover
- infrared
- image processing
- high quality
- urban areas
- hyperspectral imagery
- hyperspectral data
- spectral characteristics
- hyperspectral images
- learning machines
- spectral bands
- policy search
- aerial images
- dynamic programming
- training data
- feature selection
- remotely sensed data
- spectral images
- computer vision
- neural network