Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-Spectral Manifold Learning.
Hong HuangGuangyao ShiHaibo HeYule DuanFulin LuoPublished in: IEEE Trans. Cybern. (2020)
Keyphrases
- hyperspectral imagery
- manifold learning
- dimensionality reduction
- spectral signatures
- nonlinear dimensionality reduction
- low dimensional
- hyperspectral
- multispectral
- hyperspectral images
- high dimensional feature space
- diffusion maps
- high dimensional data
- remote sensing
- high dimensional
- hyperspectral data
- spatial resolution
- principal component analysis
- spatial information
- dimension reduction
- target detection
- feature extraction
- linear discriminant analysis
- high dimensionality
- singular value decomposition
- pattern recognition
- feature selection
- input space
- locally linear embedding
- data points
- random projections
- principal components
- sparse representation
- spatial frequency
- semi supervised
- lower dimensional
- infrared
- manifold structure
- image analysis
- feature space
- embedding space
- geodesic distance
- multiscale
- kernel trick
- image processing
- data sets