On the impact of PCA dimension reduction for hyperspectral detection of difficult targets.
Michael D. Farrell Jr.Russell M. MersereauPublished in: IEEE Geosci. Remote. Sens. Lett. (2005)
Keyphrases
- dimension reduction
- target detection
- hyperspectral
- principal component analysis
- principle component analysis
- remote sensing
- hyperspectral images
- infrared
- feature extraction
- hyperspectral data
- multispectral
- hyperspectral imagery
- dimension reduction methods
- false alarms
- random projections
- high dimensional
- dimensionality reduction
- low dimensional
- linear discriminant analysis
- hyperspectral remote sensing
- feature space
- singular value decomposition
- high dimensional data
- image data
- feature selection
- partial least squares
- cluster analysis
- high dimensionality
- unsupervised learning
- principal components
- information content
- change detection
- covariance matrix
- satellite images
- data sets
- data analysis
- target tracking
- detection rate
- independent component analysis
- mutual information
- high resolution
- lower dimensional
- image reconstruction
- image analysis
- preprocessing
- spectral signatures
- data mining