Hyperspectral and mutispectral image fusion via coupled block term decomposition with graph Laplacian regularization

Abstract

Hyperspectral super-resolution (HSR) aims at enhancing the spatial resolution of a hyperspectral image (HSI) by fusing with a higher spatial resolution multispectral image (MSI). The shared and complementary spectral-spatial information is crucial to HSR. To fully exploit the spectral-spatial correlation, as well as the intrinsic structure of the HSI and MSI, the coupled block-term decomposition (BTD) of tensor is employed to represent the data. Furthermore, the BTD is regularized by introducing a graph manifold to improve the spatial detail structures of the HR-HSI, which results in a proposed Graph Laplacian-guided Coupled Block-Term Decomposition (GLCBTD) model for the fusion of HSI-MSI. The proposed fusion framework is solved by a block coordinate descent (BCD) algorithm interleaved with the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real dataset demonstrate that the proposed GLCBTD method is superior to state-of-the-art fusion methods in preserving spatial and spectral details.