Hyperspectral Imagery Super-Resolution Based on Self-Calibrated Attention Residual Network

Hyperspectral remote sensing images are well-known for their abundant spectral characteristics to discriminate different object materials. However, due to the constraints of sensor limitations and exceedingly high acquisition costs, it is difficult to obtain high spatial resolution hyperspectral imagery. Though many methods have been focusing on the restoration of the spatial structure information, spectral information may be over-smoothed during such spatial super-resolution. In this paper, a novel self-calibrated attention residual network (SCARN) is proposed to increase spatial resolution of hyperspectral images while retain spectral consistency. In particular, a self-calibrated attention residual block (SCARB) is elaborately designed to fully exploit the spatial information and the correlation between the spectra of the hyperspectral data. Concretely, self-calibrated convolution, instead of standard convolution, is adopted to adaptively construct long-range spatial and spectral dependencies around each spatial location of hyperspectral imagery, and attention module is inserted to improve the representation ability of spectral information. Finally, global and local residual connections are designed to ease the network training difficulty and maintain a higher restoration accuracy. Experimental results over two benchmark hyperspectral datasets demonstrate the effectiveness and superiority of the proposed SCARN method against the state-of-the-art methods.