Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning.
Hong HuangGuangyao ShiHaibo HeYule DuanFulin LuoPublished in: CoRR (2018)
Keyphrases
- hyperspectral imagery
- manifold learning
- dimensionality reduction
- spectral signatures
- low dimensional
- hyperspectral
- hyperspectral images
- nonlinear dimensionality reduction
- high dimensional
- high dimensional feature space
- hyperspectral data
- diffusion maps
- high dimensional data
- multispectral
- principal component analysis
- spatial resolution
- target detection
- high dimensionality
- remote sensing
- spatial information
- feature extraction
- dimension reduction
- random projections
- data points
- pattern recognition
- locally linear embedding
- input space
- singular value decomposition
- dimensionality reduction methods
- feature space
- feature selection
- infrared
- spatial frequency
- principal components
- linear discriminant analysis
- lower dimensional
- multi band
- semi supervised
- geodesic distance
- image data
- data analysis
- manifold structure
- metric learning
- neural network
- clustering algorithm
- computer vision
- learning algorithm