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Dominant pan-sharpening frameworks simply concatenate the MS stream and thePAN stream once at a specific level. This way of fusion neglects themulti-level spectral-spatial correlation between the two streams, which isvital to improving the fusion performance. In consideration of this, we proposea Multi-level and Enhanced Spectral-Spatial Fusion Network (MESSFN) with thefollowing innovations: First, to fully exploit and strengthen the abovecorrelation, a Hierarchical Multi-level Fusion Architecture (HMFA) is carefullydesigned. A novel Spectral-Spatial (SS) stream is established to hierarchicallyderive and fuse the multi-level prior spectral and spatial expertise from theMS stream and the PAN stream. This helps the SS stream master a jointspectral-spatial representation in the hierarchical network for better modelingthe fusion relationship. Second, to provide superior expertise, consequently,based on the intrinsic characteristics of the MS image and the PAN image, twofeature extraction blocks are specially developed. In the MS stream, a ResidualSpectral Attention Block (RSAB) is proposed to mine the potential spectralcorrelations between different spectra of the MS image through adjacentcross-spectrum interaction. While in the PAN stream, a Residual Multi-scaleSpatial Attention Block (RMSAB) is proposed to capture multi-scale informationand reconstruct precise high-frequency details from the PAN image through animproved spatial attention-based inception structure. The spectral and spatialfeature representations are enhanced. Extensive experiments on two datasetsdemonstrate that the proposed network is competitive with or better thanstate-of-the-art methods. Our code can be found in github.

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