Morphological Image Distances for Hyperspectral Dimensionality Exploration using Kernel-PCA and ISOMAP.
Santiago Velasco-ForeroJesús AnguloJocelyn ChanussotPublished in: IGARSS (3) (2009)
Keyphrases
- hyperspectral
- image data
- hyperspectral images
- kernel pca
- dimensionality reduction
- input image
- laplacian eigenmaps
- image analysis
- satellite images
- hyperspectral data
- multi band
- information content
- hyperspectral imagery
- multispectral
- remote sensing
- image features
- similarity measure
- multidimensional scaling
- principal component analysis
- infrared
- image segmentation
- euclidean distance
- manifold learning
- input space
- high dimensionality
- high resolution
- feature space
- data sets
- nonlinear dimensionality reduction
- image classification
- dimension reduction
- spectral clustering
- nearest neighbor
- metric learning
- pattern recognition
- feature extraction
- feature selection