MMDN: Multi-Scale and Multi-Distillation Dilated Network for Pansharpening

Abstract

Pansharpening is a technology involving information integration and processing in remote sensing imagery. It is applied to generate a high-resolution multispectral (HRMS) image through an effective fusion of a low spatial resolution multispectral image and a panchromatic (PAN) image. In this paper, we propose an end-to-end multi-scale and multi-distillation dilated network (MMDN) for pansharpening. In MMDN, to extract more abundant spatial details from source images, a clique structure-based multi-scale dilated block (CSMDB) is presented. The clique structure in CSMDB can fully transfer the information between feature maps obtained by the multi-scale dilated convolutional filters. Then, a multi-distillation residual information block (MRIB) is constructed to help the network capture the spatial structure of different scales in MS and PAN images. Finally, to reuse and supplement the feature information, a feature embedding strategy is designed by feeding the sum result of the output of cascaded CSMDBs and the shallow features to each MRIB. Experimental results verify that the proposed MMDN outperforms other compared state-of-the-art approaches in terms of objective and subjective evaluations.