Li Shutao, Dian Renwei, Fang Leyuan, Bioucas-Dias Jose M
IEEE Trans Image Process. 2018 May 15. doi: 10.1109/TIP.2018.2836307.
Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF) based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a three-dimensional tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over current state-of-the-art HSI-MSI fusion approaches.
近年来，将低空间分辨率高光谱图像（LR-HSI）与高空间分辨率多光谱图像（HR-MSI）融合以获得高空间分辨率高光谱图像（HR-HSI）引起了越来越多的关注。在本文中，我们提出了一种基于耦合稀疏张量分解（CSTF）的方法来融合此类图像。在所提出的CSTF方法中，我们将HR-HSI视为三维张量，并将融合问题重新定义为核心张量和三种模式字典的估计。通过引入促进稀疏核心张量的正则化项，对HR-HSI中的高空间光谱相关性进行建模。字典和核心张量的估计被表述为LR-HSI和HR-MSI的耦合张量分解。在两个遥感高光谱图像上的实验证明了所提出的CSTF算法优于当前最先进的高光谱图像-多光谱图像融合方法。
