Non-Invasive Tools to Detect Smoke Contamination in Grapevine Canopies, Berries and Wine: A Remote Sensing and Machine Learning Modeling Approach.
Sigfredo FuentesEden Jane TongsonRoberta De BeiClaudia Gonzalez ViejoRenata RisticSteve TyermanKerry WilkinsonPublished in: Sensors (2019)
Keyphrases
- remote sensing
- machine learning
- change detection
- multispectral
- remote sensing images
- high spatial resolution
- hyperspectral
- high resolution
- satellite data
- satellite images
- image analysis
- remote sensing imagery
- satellite imagery
- weather prediction
- image fusion
- remote sensing data
- automatic image registration
- digital image analysis
- remotely sensed imagery
- image processing
- hyperspectral remote sensing
- hyperspectral imagery
- spectral data
- remotely sensed images
- remote sensed images
- hyperspectral images
- data analysis
- land cover
- multi spectral images
- computer vision
- earth science
- environmental sciences
- computer science
- pattern recognition
- supervised learning
- multiresolution
- earth observation
- remotely sensed data
- land cover classification
- feature extraction
- data mining