Identification and Visualization of Dominant Patterns and Anomalies in Remotely Sensed Vegetation Phenology Using a Parallel Tool for Principal Components Analysis.
Richard Tran MillsJitendra KumarForrest M. HoffmanWilliam W. HargroveJoseph P. SpruceSteven P. NormanPublished in: ICCS (2013)
Keyphrases
- remotely sensed
- principal components analysis
- land cover
- change detection
- remote sensing
- exploratory data analysis
- multispectral
- remotely sensed images
- infrared
- satellite images
- remote sensing images
- remotely sensed data
- hyperspectral
- hyperspectral images
- satellite imagery
- remote sensing data
- geographic information systems
- dimensionality reduction
- covariance matrix
- supervised classification
- principal components
- linear discriminant analysis
- geospatial data
- image data
- synthetic aperture radar
- data mining
- target detection
- tree crown
- data visualization
- support vector machine
- data analysis
- objective function
- feature extraction
- image processing
- urban areas
- sar images
- training data
- decision trees
- computer vision
- neural network