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

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 article, we propose an end-to-end multiscale and multidistillation dilated network (MMDN) for pansharpening. In MMDN, to extract more abundant spatial details from source images, a clique structure-based multiscale dilated block (CSMDB) is presented. The clique structure in CSMDB can fully transfer the information between feature maps obtained by the multiscale dilated convolutional filters. Then, a multidistillation 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.

Automated summary

Pansharpening is a remote sensing image processing technology that generates a high-resolution multispectral image by fusing low-resolution multispectral and panchromatic images. This article proposes an end-to-end multiscale and multidistillation dilated network (MMDN) for pansharpening. The MMDN utilizes a clique structure-based multiscale dilated block and a multidistillation residual information block to extract spatial details and capture the spatial structure at different scales. Experimental results demonstrate the superiority of the MMDN method in objective and subjective evaluations.