Hyperspectral image super-resolution via deep image gradient guided residual dense network

Abstract

Hyperspectral image (HSI) super-resolution attracts the great interest in remote sensing, since its effectiveness in obtaining the HSI with rich spatial information while preserving the high spectral discriminative ability, without modifying the imagery equipment. This paper proposes a novel HSI super-resolution method via gradient guided residual dense network (G-RDN), in which the spatial gradient is utilized to guide the super-resolution process. Specifically, there are three modules in the super-resolving process. Firstly, the spatial mapping between the low-resolution HSI and the desired high resolution HSI is learnt via a residual dense network. The residual dense network (RDN) is exploited to fully exploit the hierarchical features learnt from all the convolutional layers. Meanwhile, the gradient detail is extracted via a residual net (ResNet), which is further utilized to guide the super-resolution process. Finally, the fully obtained global hierarchical features is merged with the gradient details via an empirical weight. Experimental results and data analysis on three benchmark datasets show that our method achieves favorable performance.