Hyperspectral and Multispectral Image Fusion via Bayesian Nonlocal CP Factorization

Junhua Zeng, Guoxu Zhou, Yuning Qiu, Yumeng Ma, Qibin Zhao

Abstract: Recently, fusing low-resolution hyperspectral images (LR-HSIs) with high-resolution multispectral images (HR-MSIs) to obtain high-resolution HSI (HR-HSI) has become an emerging study. In this letter, Bayesian nonlocal canonical polyadic (CP) factorization (BNCPF) is proposed for fusing LR-HSI with HR-MSI, which applies CP factorization on the nonlocal tensors of HR-HSI. Compared with the vanilla scheme of applying CP factorization on HR-HSI, the nonlocal tensors reveal balanced low-rank properties along different modes, and thus CP factorization can better capture their intrinsic low-rankness. To avoid the immense CP-ranks selection on the nonlocal tensors, we develop a sparse Bayesian framework for automatic rank determination. For parameters estimation, we adopt the alternating direction method of multipliers (ADMMs) for the maximum a posteriori (MAP) estimator optimization. Experimental results verify the superiority and rank-robustness of the proposed method.