Hyperspectral Super-Resolution Using Nonlinear Unmixing and Nonnegative Tensor Factorization

Fusing a hyperspectral image (HSI) and a multispectral image (MSI) to generate a super-resolution image (SRI) with refined spatial and spectral resolution is a popular technique in hyperspectral super-resolution (HSR). Most HSR methods accomplish this task by matrix or tensor decomposition in the framework of linear unmixing. Although these methods are effective to some extent, serious challenges remain. In this work, the linear unmixing is extended to nonlinear unmixing framework and a novel HSR method based on a generalized bilinear unmixing model in tensor format is proposed. Apart from the linear part, it additionally considers bilinear interactions between endmembers. A low-rank prior is incorporated into the abundance maps and nonlinear interaction abundance maps, which can adequately model non-local similarities in images. In addition, the total variance is used to explore the local spatial relationships of the image. The optimization is implemented using the alternating direction method of multipliers (ADMM) algorithm with analytical expressions for each iterative update step, which is difficult to implement even for algorithms that focus on nonlinear unmixing. The proposed method overcomes the inherent linear limitations of the linear unmixing framework and avoids the information loss caused by matrixing the HSI and MSI with 3D-structure. The experimental results of simulations on real hyperspectral datasets demonstrate the superiority of the proposed approach over the compared HSR methods.