Locality-preserving discriminant analysis for hyperspectral image classification using local spatial information

Locality-preserving projection as well as local Fisher discriminant analysis is applied for dimensionality reduction of hyperspectral imagery based on both spatial and spectral information. These techniques preserve the local geometric structure of hyperspectral data into a low-dimensional subspace wherein a Gaussian-mixture-model classifier is then considered. In the proposed classification system, local spatial information-which is expected to be more multimodal than strictly spectral features-is used. Results with experimental hyperspectral data demonstrate that this system outperforms traditional classification approaches.