Nonlocal Coupled Tensor CP Decomposition for Hyperspectral and Multispectral Image Fusion
Résumé
Hyperspectral super-resolution, which aims at enhancing the spatial resolution of hyperspectral images (HSIs), has recently attracted considerable attention. A common way of hyperspectral super-resolution is to fuse the HSI with a higher spatial resolution multispectral image (MSI). Various approaches have been proposed to solve this problem by establishing the degradation model of low spatial resolution HSIs and MSIs based on matrix factorization methods, e.g., unmixing and sparse representation. However, this category of approaches cannot well construct the relationship between the high spatial resolution HSI and MSI. In fact, since the HSI and the MSI capture the same scene, these two image sources must have common factors. In this paper, we propose a nonlocal tensor decomposition model for HSI-MSI fusion. Firstly, the nonlocal similar patch tensors of the HSI are constructed according to the MSI, for the purpose of calculating the smooth order of all the patches for clustering. Then, the relationship between the high spatial resolution HSI and the MSI is explored through a coupled tensor canonical polyadic (CP) decomposition. The fundamental idea of the proposed model is that the factor matrices in CP decomposition of the high spatial resolution HSI's nonlocal tensor can be shared with the matrices factorized by the MSI's nonlocal tensor. Alternating direction method of multipliers is used to solve the proposed model. Through this method, the spatial structure of the MSI can be successfully transferred to the HSI. Experimental results on three synthetic datasets and one real dataset suggest that the proposed method substantially outperforms existing state-of-the-art HSI-MSI fusion methods.
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