DAMS: Dilated Attention with Multi-Stream Learning for Super-Resolution of Hyperspectral Remote Sensing Images

Hyperspectral super-resolution (SR) can effectively enhance the spatial resolution of hyperspectral images, holding significant application value. Nevertheless, existing methods tend to overlook global information and some detailed aspects of hyperspectral images, resulting in limitations in feature extraction. In addressing this issue, we propose a Dilated Attention With Multi-Stream Learning (DAMS) network to facilitate the fusion of hyperspectral and multispectral images. The network comprises three autoencoders, incorporating dilated residual multipath feature extraction for high-resolution multispectral images and a dense convolutional neural network for low-resolution hyperspectral images. Notably, no prior knowledge of point spread function (PSF) and spectral response function (SRF) is required. Experimental results with DAMS showcase its advantages over other super-resolution fusion methods, demonstrating robust performance across diverse datasets with varying PSF and SRF.