A Recurrent Feedback Hyperspectral Image Super-Resolution Reconstruction Method by Using Self-Attention-Based Pixel Awareness

Abstract

Hyperspectral images (HSIs) contain abundant spectral information, while the spatial resolution is usually limited. To obtain high-spatial-resolution HSIs, various HSI super-resolution (SR) methods are proposed. Currently, deep-learning-based SR reconstruction methods are studied in depth, which take different measures to make full use of the spatial and spectral information of HSIs, and optimize the network with lots of trainings. Although they have achieved satisfying spatial resolution, the spectral consistency before and after reconstruction is difficult to guarantee. In this article, we proposed a self-attention-based recurrent feedback network for hyperspectral SR reconstruction, utilizing pixel-aware weights and pseudo three-dimensional convolution to enhance the spatial and spectral consistency during the reconstruction process. In addition, group reconstruction is used to reduce the redundancy of information. Spectral consistency regularization is proposed to ensure the spectral consistency before and after reconstruction. The effectiveness of the proposed method is tested on one set of natural images and three hyperspectral remote sensing image datasets.

Keywords

• Feedback embedding
• hyperspectral image (HSI)
• pixel awareness
• recurrent network
• super-resolution (SR)