A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images.
Ramanarayan MohantyS. L. HappyAurobinda RoutrayPublished in: CoRR (2018)
Keyphrases
- hyperspectral images
- dimensionality reduction
- semi supervised
- manifold learning
- spectral signatures
- graph embedding
- hyperspectral
- hyperspectral imagery
- low dimensional
- subspace learning
- hyperspectral data
- lower dimensional
- nonlinear dimensionality reduction
- spectral features
- high dimensional
- spectral bands
- high dimensional data
- remote sensing
- multispectral
- band selection
- locally linear embedding
- spatial information
- feature space
- multispectral images
- hyperspectral image classification
- spectral data
- random projections
- labeled data
- principal components
- pattern recognition
- semi supervised learning
- feature extraction
- high dimensionality
- metric learning
- spatial resolution
- pairwise constraints
- pairwise
- principal component analysis
- feature selection
- linear discriminant analysis
- dimensionality reduction methods
- target detection
- image data
- active learning
- dimension reduction
- image processing
- spectral resolution