Hyperspectral image super-resolution based on attention ConvBiLSTM network

In this paper, a hyperspectral (HS) image super-resolution (SR) approach based on attention convolutional bi-long short-term memory (ConvBiLSTM) network is proposed, aiming to explore the collaborative spatial and spectral attention characteristics, thereby enhancing the spatial resolution of HS image. ConvBiLSTM network combines the spatial feature mining and sequential predicting abilities of convolutional neural network and recurrent neural network, respectively. We adapt the ConvBiLSTM network for our super-resolution purpose by regarding each band as a single frame of sequential data, and propose a band-sharing spatial-channel attention-combined ConvBiLSTM SR method to intensify the saliency features. Moreover, a spatial-regularized loss function is presented to further promote the fidelity of the super-resolved HS image. Experiments on four HS data sets show that the proposed approach outperforms some state-of-the-art HS image SR techniques, from the aspect of spectral fidelity.