Remote Sensing Hyperspectral Image Super-Resolution via Multidomain Spatial Information and Multiscale Spectral Information Fusion

Hyperspectral image super-resolution technology has made remarkable progress due to the development of deep learning. However, the technique still faces two challenges, i.e., the imbalance between spectral and spatial information extraction, and the parameter deviation and high computational effort associated with 3D convolution. In this article, we propose a super-resolution method for remote sensing hyperspectral images based on multi-domain spatial information and multi-scale spectral information fusion (MSSR). Specifically, inspired by the high degree of self-similarity of remote sensing hyperspectral images, a spatial-spectral attention module based on dilated convolution (DSSA) for capturing global spatial information is proposed. The extraction of local spatial information is then accomplished by residual blocks using small-size convolution kernels. Meanwhile, we propose the 3D Inception module to efficiently mine multi-scale spectral information. The module only retains the scale of the 3D convolution kernel in spectral dimension, which greatly reduces the high computational cost caused by 3D convolution. Comparative experimental results on four benchmark datasets demonstrate that compared with the current cutting-edge models, our method achieves state-of-the-art results and the model computation is greatly reduced.