Blind Super-Resolution of Single Remotely Sensed Hyperspectral Image

Hyperspectral image (HSI) super-resolution has recently advanced with significant progress by utilizing the powerful representation capabilities of deep neural networks (DNNs). These approaches, however, inevitably rely on a sizable amount of training data which can be difficult to acquire for remotely sensed HSIs. In many cases, these methods are designed and tailored for only one or a few specific super-resolution scenarios, making them inflexible for handling images with different unknown degradations. In this article, we introduce a two-step framework for blind remotely sensed HSI super-resolution, where the degradation is unknown. Specifically, in the first step, we propose to leverage the abundant remotely sensed color images to address the data insufficiency for remotely sensed HSI super-resolution. It is achieved by exploring the spatial knowledge from remotely sensed color images with a super-resolution network for a predefined degradation, which is then transferred to HSIs via band-by-band super-resolution. Direct use of the results from the transferred super-resolution network is suboptimal as it neglects the spectral correlations of different bands and the gap between predefined degradation and the real one. To make further refinements, we present an unsupervised scheme that simultaneously refines the super-resolved HSI and the unknown degradation by a nonnegative matrix factorization network and a learnable degradation prior. To validate the effectiveness of our method, we conducted extensive experiments on a variety of remotely sensed HSI datasets. The results demonstrate that our method could generalize on various unknown degradations with superior performance against the state-of-the-art methods.