Tensor-based offset-sparsity decomposition for hyperspectral image classification

In this paper, the tensor-based offset-sparsity decomposition (TOSD) method, or low-rank and sparse decomposition, is applied to hyperspectral imagery, where the low-rank tensor is considered to be enhanced or pruned data and used for classification. In the tensor form of dataset, all the information of the original 3D data cube, includes spatial and spectral information, can be better reserved. To make the low-rank assumption more possibly true, spatial and spectral segmentations are conducted in a preprocessing step for the TOSD. The experimental results demonstrate the TOSD offers better performance than the matrix-based one, and the spatial-spectral segmentation can further improve the performance.