Prediction of chlorophyll-a and suspended solids through remote sensing and artificial neural networks.
Lucas Silveira KupssinsküTainá Thomassim GuimarãesRafael de FreitasEniuce Menezes De SouzaPedro RossaAdemir Marques JuniorMaurício Roberto VeronezLuiz Gonzaga Jr.Caroline Lessio CazarinFrederico Fábio MauadPublished in: ICST (2019)
Keyphrases
- remote sensing
- artificial neural networks
- spectral data
- change detection
- multispectral
- remote sensing images
- weather prediction
- image analysis
- remote sensing imagery
- high resolution
- satellite images
- satellite imagery
- image processing
- satellite data
- remote sensing data
- hyperspectral
- image fusion
- neural network
- automatic image registration
- digital image analysis
- remotely sensed data
- multi spectral images
- high resolution satellite images
- remotely sensed imagery
- earth science
- remotely sensed images
- earth observation
- pattern recognition
- high spatial resolution
- hyperspectral images
- remotely sensed
- hyperspectral imaging
- hyperspectral remote sensing
- remote sensing image processing
- land cover classification
- hyperspectral imagery
- climate change