Hyperspectral super-resolution via nonlinear unmixing

Abstract

Fusing a hyperspectral image (HSI) with a multispectral image (MSI) to produce a super-resolution image (SRI) that possesses both fine spatial and spectral resolutions is a widely adopted technique in hyperspectral super-resolution (HSR). Most existing HSR methods accomplish this task within the framework of linear mixing model (LMM). However, a severe challenge lies in the inherent linear constraint of LMM, which hinders the adaptability of these HSR methods to complex real-world scenarios. In this work, the LMM is extended to the generalized bilinear model (GBM), and a novel HSR method based on nonnegative tensor factorization is proposed in the framework of nonlinear unmixing. Apart from the linear part, it additionally considers the main nonlinear interactions, that is, the bilinear interactions between the endmembers. Crucially, each potential decomposition factor possesses a physical interpretation, enabling the incorporation of prior information to enhance reconstruction performance. Furthermore, an HSR algorithm has been devised specifically for scenarios where the spatial degradation operators from SRI to HSI are unknown, which undoubtedly enhances its practical applicability. The proposed methods overcome the inherent linear limitations of the LMM framework while avoiding the information loss associated with matrixizing HSI and MSI. The effectiveness of the proposed methods is showcased through simulated and real data.