Deep Intra Fusion for Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) super-resolution is currently attracting great interest in remote sensing, since it allows the generation of high spatial resolution HSIs and circumventing the main limitation of the imagery sensors. This paper proposes a novel deep intra fusion network (IFN) for the HSI super-resolution, in which both the spatial and the spectral information have been fully and automatically exploited. Specifically, parallel convolutions are applied to two adjacent bands and their difference band, and obtain the high-dimensional features. Meanwhile, an automatically aggregation module is applied in the IFN to achieve the intra-fusion between these features. In this way, both the spatial information of the current band and the spectral information between neighboring bands are utilized in the super-resolving process. Experimental results and data analysis suggest the effectiveness of the proposed method.