Multiple Frame Splicing and Degradation Learning for Hyperspectral Imagery Super-Resolution

Hyperspectral Imagery (HSI) is an emerging remote sensing technology to discriminate different remote sensing objects. However, the HSI spatial resolution is relatively low due to the trade-off in restricted physical hardware and various imaging conditions, restricting the subsequent object detection applications. At present, the Single Hyperspectral Super Resolution (SHSR) strategy has encountered the bottleneck on more precise details extraction, and the Fusion Hyperspectral Image Super Resolution (FHSR) strategy must need extra RGB/multispectral information which is not suitable for general HSI usage. Also, both types of current strategies focus less on the multiple degradation causes of low spatial resolution. In this paper, a step forward in designing a novel framework of multiple frame splicing strategy to greatly improve the SHSR quality, and applying multiple HSI degradation models to better fit the real degradation circumstance. Specifically, the framework is an end-to-end Super Resolution (SR) network that supersedes a single up-sampling module and removes complex attention residual model due to the same size of multiple splicing low-resolution input samples with high-resolution outputs. The effective framework will alleviate the vague at higher multiples, and accelerate the training convergence. Based on this framework, multiple degradation low-resolution samples can be simultaneously combined to fit better for the blind super-resolution result. Concretely, the degradation focus on the blur, noise, compression, and their combinations to simulate the real degradation. Experimental results on three different hyperspectral datasets demonstrate that the proposed MFSDM algorithm can significantly enhance the details in the recovered high-resolution hyperspectral images, and outperforms the state-of-the-art SHSR methods.