Unsupervised Deep Hyperspectral Super-Resolution With Unregistered Images

Fusion based hyperspectral image (HSI) super-resolution has long been the research focus of hyperspectral image processing since it can generate a high-resolution (HR) HSI in both spatial and spectral domains. However, the success of the existing fusion based HSI super-resolution methods depends on the premise that the images utilized for fusion (i.e. the input low-spatial-resolution HSI and the low-spectral-resolution multispectral image) are exactly registered. Although such a premise is too idealistic to comply with in real cases, few efforts have considered this problem. To fill this gap, we propose to incorporate image registration into HSI super-resolution for joint unsupervised learning in this study. Specifically, a spatial transformer network (STN) is introduced to learn the parameters of the affine transformation between the input two images. In order to avoid over-fitting, we constrain the STN with a novel constraint during learning. By doing this, both the STN and super-resolution network can be cast into a weighted joint learning model without any supervision from the latent HR HSI. Experimental results demonstrate the effectiveness of the proposed method in coping with unregistered input images.