Stochastic Higher-Order Independent Component Analysis for Hyperspectral Dimensionality Reduction.
Daniela LupuIon NecoaraJoseph L. GarrettTor Arne JohansenPublished in: IEEE Trans. Computational Imaging (2022)
Keyphrases
- independent component analysis
- hyperspectral
- dimensionality reduction
- higher order
- principal component analysis
- hyperspectral images
- remote sensing
- multispectral
- hyperspectral data
- independent components
- infrared
- principal components
- target detection
- random projections
- high dimensional data
- factor analysis
- hyperspectral imagery
- low dimensional
- hyperspectral remote sensing
- signal processing
- spatial resolution
- image data
- feature space
- information content
- natural images
- dimensionality reduction methods
- blind source separation
- high dimensional
- satellite images
- feature extraction
- dimension reduction
- linear discriminant analysis
- principle component analysis
- input space
- high dimensionality
- lower dimensional
- machine learning
- subspace projection
- kernel pca
- markov random field
- singular value decomposition
- pattern recognition
- face recognition
- covariance matrix
- image processing
- feature selection
- neural network
- discriminant analysis
- sparse representation
- data points
- image analysis
- spectral signatures