DDLPS: Detail-Based Deep Laplacian Pansharpening for Hyperspectral Imagery

Abstract

In this paper, we propose a new pansharpening method called detail-based deep Laplacian pansharpening (DDLPS) to improve the spatial resolution of hyperspectral imagery. This method includes three main components: upsampling, detail injection, and optimization. In particular, a deep Laplacian pyramid super-resolution network (LapSRN) improves the resolution of each band. Then, a guided image filter and a gain matrix are used to combine the spatial and spectral details with an optimization problem, which is formed to adaptively select an injection coefficient. The DDLPS method is compared with 11 state-of-the-art or traditional pansharpening approaches. The experimental results demonstrate the superiority of the DDLPS method in terms of both quantitative indices and visual appearance. In addition, the training of LapSRN is based on the data sets of traditional RGB images, which overcomes the practical difficulty of insufficient training samples for pansharpening.