Multiband Image Fusion via Regularization on a Riemannian Submanifold

Abstract

Multiband image fusion aims to generate high spatial resolution hyperspectral images by combining hyperspectral, multispectral or panchromatic images. However, fusing multiband images remains a challenge due to the identifiability and tracking of the underlying subspace across varying modalities and resolutions. In this paper, an efficient multiband image fusion model is proposed to investigate the latent structures and intrinsic physical properties of a multiband image, which is characterized by the Riemannian submanifold regularization method, nonnegativity and sum-to-one constraints. An alternating minimization scheme is proposed to recover the latent structures of the subspace via the manifold alternating direction method of multipliers (MADMM). The subproblem with Riemannian submanifold regularization is tackled by the projected Riemannian trust-region method with guaranteed convergence. The effectiveness of the proposed method is demonstrated on two multiband image fusion problems: (1) hyperspectral and panchromatic image fusion and (2) hyperspectral, multispectral and panchromatic image fusion. The experimental results confirm that our method demonstrates superior fusion performance with respect to competitive state-of-the-art fusion methods.