Spatial-Spectral Middle Cross-Attention Fusion Network for Hyperspectral Image Superresolution

Abstract

The spatial and spectral features of hyperspectral images exhibit complementarity, and neglecting them prevents the full exploitation of useful information for superresolution. This article proposes a spatial-spectral middle cross-attention fusion network to explore the spatial-spectral structure correlation. Initially, we learn spatial and spectral features through spatial and spectral branches instead of single ones to reduce information compression. Then, a novel middle-cross attention fusion block that includes middle features fusion strategy and cross-attention is proposed to fuse spatial-spectral features to enhance their mutual effects, which aims to explore the spatial-spectral structural correlations. Finally, we propose a spectral feature compensation mechanism to provide complementary information for adjacent band groups. The experimental results show that the proposed method outperforms state-of-the-art algorithms in object values and visual quality.