Hyperspectral Dimensionality Reduction Based on Multiscale Superpixelwise Kernel Principal Component Analysis.
Lan ZhangHongjun SuJingwei ShenPublished in: Remote. Sens. (2019)
Keyphrases
- hyperspectral
- kernel principal component analysis
- multiscale
- kernel pca
- dimensionality reduction
- principal components
- principal component analysis
- feature extraction
- high dimensional
- remote sensing
- feature space
- hyperspectral images
- linear discriminant analysis
- multispectral
- infrared
- discriminant analysis
- input space
- image data
- kernel methods
- high dimensional feature space
- high dimensional data
- information content
- image processing
- scale space
- kernel function
- low dimensional
- kernel matrix
- dimension reduction
- random projections
- high dimensionality
- spectral clustering
- data points
- pattern recognition
- feature selection
- satellite images
- metric learning
- computer vision
- lower dimensional
- singular value decomposition
- support vector machine
- feature vectors
- machine learning
- sparse representation
- mutual information
- high resolution
- kernel learning
- preprocessing
- clustering algorithm