Hyperspectral Image Super-Resolution via Recurrent Feedback Embedding and SpatialSpectral Consistency Regularization

Hyperspectral images with tens to hundreds of spectral bands usually suffer from low spatial resolution due to the limitation of the amount of incident energy. Without auxiliary images, the single hyperspectral image super-resolution (SR) method is still a challenging problem because of the high-dimensionality characteristic and special spectral patterns of hyperspectral images. Failing to thoroughly explore the coherence among hyperspectral bands and preserve the spatialx2013;spectral structure of the scene, the performance of existing methods is still limited. In this article, we propose a novel single hyperspectral image SR method termed RFSR, which models the spectrum correlations from a sequence perspective. Specifically, we introduce a recurrent feedback network to fully exploit the complementary and consecutive information among the spectra of the hyperspectral data. With the group strategy, each grouping band is first super-resolved by exploring the consecutive information among groups via feedback embedding. For better preservation of the spatialx2013;spectral structure among hyperspectral data, a regularization network is subsequently appended to enforce spatialx2013;spectral correlations over the intermediate estimation. Experimental results on both natural and remote sensing hyperspectral images demonstrate the advantage of our approach over the state-of-the-art methods.

Automated summary

In this article, a novel single hyperspectral image super-resolution (SR) method called RFSR is proposed. The method models the spectrum correlations from a sequence perspective and utilizes a recurrent feedback network to fully exploit the information among the spectra of the hyperspectral data. Experimental results demonstrate the advantage of the proposed approach over the state-of-the-art methods.