A Group-Based Embedding Learning and Integration Network for Hyperspectral Image Super-Resolution

Although natural image super-resolution methods have achieved impressive performance, single hyperspectral image super-resolution still remains a challenge due to the high dimensionality. In recent years, many single hyperspectral image super-resolution methods adopted the group-convolution strategy to design the network for reducing the computational burden. However, these methods still process all spectral bands at once during the deep feature extraction and reconstruction, which increases the difficulty of fully exploring the inherent data characteristic of hyperspectral images. Moreover, the advanced group-based methods make insufficient exploitation of complementary information contained in different bands, resulting in limited reconstruction performance. In this article, we propose a novel group-based single hyperspectral image super-resolution method termed group-based embedding learning and integration network (GELIN) to reconstruct high-resolution images in a group-by-group manner, which alleviates the difficulty of feature extraction and reconstruction for hyperspectral images. Specifically, a spatial-spectral embedding learning module is designed to extract rewarding spatial details and explore the correlations among spectra simultaneously. Considering the high similarity among different bands, a neighboring group integration module is proposed to fully exploit the complementary information contained in neighboring image groups to recover missing details in the target image group. Experimental results on both natural and remote sensing hyperspectral datasets demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.

Automated summary

The article proposes a novel group-based single hyperspectral image super-resolution method, GELIN, which reconstructs high-resolution images in a group-by-group manner to alleviate the difficulty of feature extraction and reconstruction for hyperspectral images. Experimental results demonstrate that the proposed method is superior to other state-of-the-art methods both visually and metrically.