Genetic algorithms and Linear Discriminant Analysis based dimensionality reduction for remotely sensed image analysis.
Minshan CuiSaurabh PrasadMajid MahrooghyLori M. BruceJames V. AanstoosPublished in: IGARSS (2011)
Keyphrases
- linear discriminant analysis
- remotely sensed
- dimensionality reduction
- image analysis
- remote sensing
- multispectral
- change detection
- pattern recognition
- discriminant analysis
- remotely sensed images
- hyperspectral
- satellite images
- principal component analysis
- high dimensional data
- infrared
- dimension reduction
- remote sensing images
- hyperspectral images
- high dimensional
- high dimensionality
- small sample size
- feature space
- neural network
- dealing with high dimensional data
- satellite imagery
- data representation
- feature extraction
- null space
- low dimensional
- principal components
- class separability
- feature selection
- land cover
- data points
- multispectral images
- linear projection
- dimensionality reduction methods
- random projections
- computer vision
- unsupervised learning
- lower dimensional
- linear discriminant
- singular value decomposition
- kernel pca
- kernel discriminant analysis
- supervised dimensionality reduction
- image processing
- feature vectors
- image data
- geospatial data
- face recognition
- synthetic aperture radar
- fisher criterion
- scale space
- graph embedding
- scatter matrices
- least squares
- machine learning