LAUSR

Tensor-based dimensionality reduction (DR) of hyperspectral images is a promising research topic. However, patch-based tensorization usually adopts a squared neighborhood with fixed window size, which may be inaccurate in modeling the local spatial information in a hyperspectral image scene. In this work, we propose a novel shape-adaptive tensor factorization (SATF) model for dimensionality reduction and classification of hyperspectral images. Firstly, shape-adaptive patch features are extracted to build fourth-order tensors. Secondly, multilinear singular value decomposition (MLSVD) is adopted for tensor factorization and latent features are extracted via mode- $i$ tensor-matrix product. Finally, classification is conducted by using a sparse multinomial logistic regression (SMLR) model. Experimental results, conducted with two popular hyperspectral data sets collected over the Indian Pines and the University of Pavia, respectively, indicate that the proposed method outperforms the other traditional and tensor-based DR methods.