Dilated projection correction network based on autoencoder for hyperspectral image super-resolution

This paper focuses on improving the spatial resolution of the hyperspectral image (HSI) by taking the prior information into consideration. In recent years, single HSI super-resolution methods based on deep learning have achieved good performance. However, most of them only simply apply general image super-resolution deep networks to hyperspectral data, thus ignoring some specific characteristics of hyperspectral data itself. In order to make full use of spectral information of the HSI, we transform the HSI SR problem from the image domain into the abundance domain by the dilated projection correction network with an autoencoder, termed as aeDPCN. In particular, we first encode the low-resolution HSI to abundance representation and preserve the spectral information in the decoder network, which could largely reduce the computational complexity. Then, to enhance the spatial resolution of the abundance embedding, we super-resolve the embedding in a coarse-to-fine manner by the dilated projection correction network where the back-projection strategy is introduced to further eliminate spectral distortion. Finally, the predictive images are derived by the same decoder, which increases the stability of our method, even at a large upscaling factor. Extensive experiments on real hyperspectral image scenes demonstrate the superiority of our method over the state-of-the-art, in terms of accuracy and efficiency. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a method for hyperspectral image super-resolution through abundance domain transformation using deep learning and autoencoder. Experimental results demonstrate the superior accuracy and efficiency of the method over the state-of-the-art.