Blind Unmixing of Hyperspectral Images Based on L1 Norm and Tucker Tensor Decomposition

Most of the traditional hyperspectral unmixing methods are based on the matrix and often ignore the spatial information of hyperspectral images (HSIs). In recent years, tensor-based methods have been gradually used in hyperspectral unmixing, owing to their ability to completely preserve the real spatial structure of HSIs. A blind unmixing method for HSIs based on an L-1 regular term and tucker tensor decomposition (BUTTDL1) is proposed, which describes the low rank of abundance by tucker tensor decomposition in the form of a third-order tensor and increases the sparse characterization of abundance by the L-1 norm. A comparative experiment is performed on two simulation datasets. Compared with the latest method unmixing with low-rank tensor regularization algorithm accounting for endmembers variability (ULTRA-V), in the simulation dataset Data Cube 1 (DC1), the endmember mean square error (MSE) of BUTTDL1 is decreased by 1.1, and the abundance MSE is decreased by 8.6. In the simulation dataset DC2, the endmember MSE is decreased by 2.4, and the abundance MSE is decreased by 6.63.

Automated summary

A novel blind unmixing method for hyperspectral images based on tucker tensor decomposition and L-1 regularization term was proposed, outperforming the latest method ULTRA-V in comparative experiments on two simulation datasets.