MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening

Abstract

Through fusing low resolution Multi-Spectral (MS) images and high resolution PANchromatic (PAN) images, pan-sharpening provides a satisfactory solution for the demands of high resolution multi-spectral images. However, dominant pan-sharpening frameworks simply concatenate the MS stream and the PAN stream once at a specific level. This way of fusion neglects the multi-level spectral-spatial correlation between the two streams, which is vital to improving the fusion performance. In consideration of this, we propose a Multi-level and Enhanced Spectral-Spatial Fusion Network (MESSFN) with the following innovations: First, to fully exploit and strengthen the above correlation, a Hierarchical Multi-level Fusion Architecture (HMFA) is carefully designed. A novel Spectral-Spatial (SS) stream is established to hierarchically derive and fuse the multi-level prior spectral and spatial expertise from the MS stream and the PAN stream. This helps the SS stream master a joint spectral-spatial representation in the hierarchical network for better modeling the fusion relationship. Second, to provide superior expertise, consequently, based on the intrinsic characteristics of the MS image and the PAN image, two feature extraction blocks are specially developed. In the MS stream, a Residual Spectral Attention Block (RSAB) is proposed to mine the potential spectral correlations between different spectra