LAUSR

This letter explores a spectral–spatial tensor-based dimensionality reduction (DR) method to cope with hyperspectral image (HSI) feature extraction and classification. This method uses the Gabor filter banks as the bias spectral–spatial feature hybrider and further integrates the tensor-based alignment strategy for the discriminant locality with sparse factorization by extracting optimal spectral–spatial features and simultaneously maintaining structural relevance. Comparative experimental results with two real HSIs demonstrate that the proposed DR method has a considerable advantage over other traditional feature extraction methods.