A Spectral Grouping and Attention-Driven Residual Dense Network for Hyperspectral Image Super-Resolution

Although unprecedented success has been achieved in convolutional neural network (CNN)-based super-resolution (SR) for natural images, hyperspectral image (HSI) SR without auxiliary high-resolution images remains a challenging task due to the high spectral dimensionality, where learning effective spatial and spectral representations is of great importance. In this article, we introduce a novel CNN-based HSI SR method, termed spectral grouping and attention-driven residual dense network (SGARDN) to facilitate the modeling of all spectral bands and focus on the exploration of spatial-spectral features. Considering the block characteristic of HSI, we employ group convolutions in and between groups composed of highly similar spectral bands at early stages to extract informative spatial features and avoid spectral disorder caused by normal convolution. To exploit spectral prior, a new spectral attention mechanism constructed by covariance statistics of features is designed to adaptively recalibrate features. We adapt the spectral attention for group convolutions to rescale grouping features with holistic spectral information. These two sequential operations called spectral grouping and integration module aim to extract effective shallow spatial-spectral features that are reused in the following layers. On the other hand, the residual dense block can better deal with spatial-spectral features by experimental comparison and hence is combined with the spectral attention to form a new basic building block for powerful feature expression and spectral correlation learning. The experimental results on synthesized and real-scenario HSIs demonstrate the feasibility and superiority of the proposed method over other state-of-the-art methods.

Automated summary

A novel CNN-based method, SGARDN, is proposed in this article for hyperspectral image super-resolution, utilizing spectral grouping and attention mechanisms to extract effective spatial-spectral features and improve feature expression and spectral correlation learning. Experimental results demonstrate the superiority of the proposed method over other state-of-the-art methods in synthesized and real-scenario hyperspectral images.