Fusing a low spatial resolution hyperspectral image (HSI) with a high spatial resolution multispectral image (MSI), aiming to produce a super-resolution hyperspectral image, has recently attracted increasing research interest. In this paper, a novel approach based on coupled non-negative tensor decomposition is proposed. The proposed method performs a tucker tensor factorization of a low resolution hyperspectral image and a high resolution multispectral image under the constraint of non-negative tensor decomposition (NTD). The conventional matrix factorization methods essentially lose spatio-spectral structure information when stacking the 3D data structure of a hyperspectral image into a matrix form. Moreover, the spectral, spatial, or their joint structural features have to be imposed from the outside as a constraint to well pose the matrix factorization problem. The proposed method has the advantage of preserving the spatio-spectral structure of hyperspectral images. In this paper, the NTD is directly imposed on the coupled tensors of the HSI and MSI. Hence, the intrinsic spatio-spectral structure of the HSI is represented without loss, and spatial and spectral information can be interdependently exploited. Furthermore, multilinear interactions of different modes of the HSIs can be exactly modeled with the core tensor of the Tucker tensor decomposition. The proposed method is straightforward and easy to implement. Unlike other state-of-the-art approaches, the complexity of the proposed approach is linear with the size of the HSI cube. Experiments on two well-known datasets give promising results when compared with some recent methods from the literature.

中文翻译：

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使用耦合非负 Tucker 张量分解的高光谱和多光谱图像融合

将低空间分辨率高光谱图像 (HSI) 与高空间分辨率多光谱图像 (MSI) 融合，旨在产生超分辨率高光谱图像，最近引起了越来越多的研究兴趣。在本文中，提出了一种基于耦合非负张量分解的新方法。所提出的方法在非负张量分解（NTD）的约束下对低分辨率高光谱图像和高分辨率多光谱图像执行塔克张量分解。传统的矩阵分解方法在将高光谱图像的 3D 数据结构堆叠成矩阵形式时，本质上会丢失空间光谱结构信息。此外，光谱、空间、或者它们的联合结构特征必须从外部强加为约束，才能很好地提出矩阵分解问题。所提出的方法具有保留高光谱图像的空间光谱结构的优点。在本文中，NTD 直接施加在 HSI 和 MSI 的耦合张量上。因此，HSI 的内在空间光谱结构可以无损地表示，并且可以相互依赖地利用空间和光谱信息。此外，可以使用 Tucker 张量分解的核心张量对 HSI 不同模式的多线性相互作用进行精确建模。所提出的方法简单易行。与其他最先进的方法不同，所提出方法的复杂性与 HSI 立方体的大小呈线性关系。