Deep Unsupervised Blind Hyperspectral and Multispectral Data Fusion

Hyperspectral images (HSIs) usually have finer spectral resolution but coarser spatial resolution than multispectral images (MSIs). To obtain a desired HSI with higher spatial resolution, great research attention has been paid to achieving hyperspectral super-resolution by fusing the observed HSI with an auxiliary MSI of the same scene. However, most of the existing HSI-MSI fusion methods rely either on prior knowledge of the degradation model or on sufficient training data, hindering their practicality and interpretability. In this letter, we propose a novel unsupervised HSI-MSI fusion network with the ability of degradation adaptive learning, namely, UDALN. Specifically, we propose three modules to straightly encode the spatial and spectral transformations across resolutions, i.e., SpaDnet, SpeUnet, and SpeDnet. Through an elaborately designed three-stage unsupervised training strategy, the estimated network parameters can exhibit clear physical meanings of degradation processes and therefore help guarantee a faithful reconstruction of the desired HSI. The experimental results on two widely used hyperspectral datasets demonstrate the effectiveness of our method in comparison to the state-of-the-art HSI-MSI fusion models. (Code available at https://github.com/JiaxinLiCAS/UDALN_GRSL.)

Automated summary

This letter proposes a novel unsupervised HSI-MSI fusion network with degradation adaptive learning capability. The network parameters can exhibit clear physical meanings of degradation processes through specific encoding modules and training strategies, resulting in accurate reconstruction of the desired HSI.