Noise-Adjusted Principle Component Analysis For Hyperspectral Remotely Sensed Imagery Visualization.
Shangshu CaiQian DuRobert J. MoorheadMahnas Jean Mohammadi-AraghDerek IrbyPublished in: IEEE Visualization (2005)
Keyphrases
- hyperspectral
- principle component analysis
- remotely sensed
- remote sensing
- hyperspectral data
- hyperspectral images
- satellite images
- multispectral
- infrared
- hyperspectral imagery
- dimension reduction
- target detection
- independent component analysis
- spatial resolution
- image data
- random projections
- remote sensing images
- lower dimensional
- change detection
- feature extraction
- face recognition
- image analysis
- image processing
- svm classifier
- principal component analysis
- high spatial resolution
- high resolution
- data analysis
- land cover
- feature space
- remotely sensed images
- similarity measure
- information content
- high quality
- covariance matrix
- neural network
- object recognition
- dimensionality reduction
- least squares
- nearest neighbor
- high dimensional
- machine learning