Sparse dimensionality reduction of hyperspectral image based on semi-supervised local Fisher discriminant analysis.
Zhenfeng ShaoLei ZhangPublished in: Int. J. Appl. Earth Obs. Geoinformation (2014)
Keyphrases
- hyperspectral
- dimensionality reduction
- semi supervised
- random projections
- hyperspectral data
- supervised dimensionality reduction
- high dimensional
- remote sensing
- hyperspectral images
- multispectral
- fisher discriminant analysis
- infrared
- sparse representation
- graph embedding
- kernel trick
- hyperspectral remote sensing
- metric learning
- pixel classification
- image data
- target detection
- hyperspectral imagery
- low dimensional
- hyperspectral image classification
- high dimensionality
- hyperspectral imaging
- high dimensional data
- principal component analysis
- spatial resolution
- data points
- dimensionality reduction methods
- satellite images
- feature extraction
- kernel learning
- linear discriminant analysis
- spectral signatures
- lower dimensional
- active learning
- information content
- least squares
- feature selection
- data sets
- singular value decomposition
- feature space
- pattern recognition
- reflectance spectra
- dimension reduction
- discriminant analysis
- spectral data
- spectral bands
- computer vision
- pairwise constraints
- input space
- principal components
- high resolution