Spatial-Spectral Feedback Network for Super-Resolution of Hyperspectral Imagery

Recently, single gray/RGB image super-resolution (SR) methods based on deep learning have achieved great success. However, there are two obstacles to limit technical development in the single hyperspectral image super-resolution. One is the high-dimensional and complex spectral patterns in hyperspectral image, which make it difficult to explore spatial information and spectral information among bands simultaneously. The other is that the number of available hyperspectral training samples is extremely small, which can easily lead to overfitting when training a deep neural network. To address these issues, in this paper, we propose a novel Spatial-Spectral Feedback Network (SSFN) to refine low-level representations among local spectral bands with high-level information from global spectral bands. It will not only alleviate the difficulty in feature extraction due to high dimensional of hyperspectral data, but also make the training process more stable. Specifically, we use hidden states in an RNN with finite unfoldings to achieve such feedback manner. To exploit the spatial and spectral prior, a Spatial-Spectral Feedback Block (SSFB) is designed to handle the feedback connections and generate powerful high-level representations. The proposed SSFN comes with a early predictions and can reconstruct the final high-resolution hyperspectral image step by step. Extensive experimental results on three benchmark datasets demonstrate that the proposed SSFN achieves superior performance in comparison with the state-of-the-art methods. The source code is available at https://github.com/tangzhenjie/SSFN.