Dual 1D-2D Spatial-Spectral CNN for Hyperspectral Image Super-Resolution

Hyperspectral image (HSI) spatial super-resolution(SR) is a challenging task. Compared with a RGB images, the mapping between the low-high HSI pairs is more difficult since much more spectral bands are involved. In this paper, a novel dual 1D-2D spatial-spectral convolutional neural network (CNN) architecture is proposed for spatial SR of HSIs. Specifically, by differential treatment over redundancy in spectral and spatial domains of an HSI, the spectral and spatial context are first separately explored by 1D and 2D convolution. These two kinds of feature information are then fused using a novel hierarchical side connection, which impose the spectral information to the spatial path gradually. Experimental results over benchmark Pavia data set demonstrate that the proposed architecture clearly outperform state-of-the-art 3D CNN based works in terms of both visual quality and quantitative assessment.