TENSOR LOCALITY PRESERVING PROJECTION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

By considering the cubic nature of hyperspectral image (HSI) and to address the issue of the curse of dimensionality, we introduce a tensor locality preserving projection (TLPP) algorithm for HSI classification. TLPP has been proved to be effective in preserving the geometrical structure of data for dimensionality reduction. More importantly, data can be taken directly in the form of a tensor of arbitrary order as input, such that the damage to sample's geometrical structure is avoided during vectorizing. For the HSI classification, TLPP can effectively embed both spatial structure and spectral information into low-dimensional space simultaneously by a series of projection matrices trained for each mode of input samples. The experimental results on the AVIRIS hyperspectral image confirm the effectiveness of TLPP.