Fast and Latent Low-Rank Subspace Clustering for Hyperspectral Band Selection.
Weiwei SunJiangtao PengGang YangQian DuPublished in: IEEE Trans. Geosci. Remote. Sens. (2020)
Keyphrases
- low rank
- subspace clustering
- hyperspectral
- band selection
- high dimensional data
- hyperspectral images
- hyperspectral imagery
- hyperspectral remote sensing
- hyperspectral data
- remote sensing
- pixel classification
- spectral bands
- multispectral
- nearest neighbor
- hyperspectral imaging
- infrared
- target detection
- high dimensional
- dimensionality reduction
- image data
- high dimensionality
- low dimensional
- data analysis
- data points
- data sets
- dimension reduction
- latent variables
- random projections
- input space
- information content
- spatial resolution
- satellite images
- multi band
- clustering method
- matrix factorization
- sparse representation
- clustering algorithm
- original data
- singular value decomposition
- semi supervised
- input data
- pattern recognition
- machine learning
- spectral resolution
- computer vision
- linear subspace
- lower dimensional
- change detection