Blind Hyperspectral and Multispectral Image Fusion Using Coupled
Non-Negative Tucker Tensor Decomposition
Fusing a low spatial resolution hyperspectral image (HSI) with a high
spatial resolution multispectral image (MSI) to produce a fused high
spatio-spectal resolution one, referred to as HSI super-resolution, has
recently attracted increasing research interests. In this paper, a new
method based on coupled non-negative tensor decomposition (CNTD) is
proposed. The proposed method uses tucker tensor factorization for low
resolution hyperspectral image (LR-HSI) and high resolution
multispectral image (HR-MSI) under the constraint of non-negative tensor
ecomposition (NTD). The conventional non-negative matrix factorization
(NMF) method essentially loses spatio-spectral joint structure
information when stacking a 3D data into a matrix form. On the contrary,
in NMF-based methods, the spectral, spatial, or their joint structures
must be imposed from outside as a constraint to well pose the NMF
problem, The proposed CNTD method blindly brings the advantage of
preserving the spatio-spectral joint structure of HSIs. In this paper,
the NTD is imposed on the coupled tensor of HIS and MSI straightly.
Hence the intrinsic spatio-spectral joint structure of HSI can be
losslessly expressed and interdependently exploited. Furthermore,
multilinear interactions of different modes of the HSIs can be exactly
modeled by means of the core tensor of the Tucker tensor decomposition.
The proposed method is completely straight forward and easy to
implement. Unlike the other state-of-the-art methods, the complexity of
the proposed CNTD method is quite linear with the size of the HSI cube.
Compared with the state-of-the-art methods experiments on two well-known
datasets, give promising results with lower complexity order.