Hyper-Spectral Image Compression By Joint Spatial Spectral Dimension Reduction Using Thresholded Principal Component Analysis.
Liel KapahNoy WeizmanDima BykhovskyIsaac AugustPublished in: WHISPERS (2022)
Keyphrases
- dimension reduction
- hyperspectral
- principal component analysis
- image compression
- hyperspectral data
- random projections
- remote sensing
- spectral data
- hyperspectral images
- infrared
- principal components
- hyperspectral imagery
- multispectral
- partial least squares
- low dimensional
- linear discriminant analysis
- face recognition
- spectral bands
- image data
- feature extraction
- dimension reduction methods
- dimensionality reduction
- independent component analysis
- spectral resolution
- singular value decomposition
- high dimensional
- feature space
- high dimensional data
- wavelet transform
- manifold learning
- information content
- covariance matrix
- image processing
- high dimensionality
- data sets
- unsupervised learning
- remote sensing imagery
- satellite images
- feature selection
- cluster analysis
- lower dimensional
- principle component analysis
- active learning
- image analysis
- preprocessing
- high quality
- training data