Differential Strategy-Based Multi-Level Dense Network for Pansharpening

Abstract

Due to the discrepancy in spatial structure between multispectral (MS) and panchromatic (PAN) images, the general fusion scheme will lead to image error in the fused result. To solve this issue, a differential strategy-based multi-level dense network is proposed, and it regards the image pairs at different scales as the input of the network at different levels and is able to map the spatial information in PAN images to each band of MS images well by learning the differential information of different levels, which effectively solves the scale effect of remote sensing images. An improved dense network with the same hierarchical structure is used to obtain richer spatial features to enhance the spatial information of the fused result. Meanwhile, a hybrid loss strategy is used to constrain the network at different levels for obtaining better results. Qualitative and quantitative analyses show that the result has a uniform spectral distribution, a complete spatial structure, and optimal evaluation criteria, which fully demonstrate the superior performance of the proposed method.