DADR: Dual Attention Based Dual Regression Network for Remote Sensing Image Pan-Sharpening

In order to solve the single imaging problem of remote sensing satellites, pan-sharpening technology is proposed. By fusing multispectral (MS) images and panchromatic (PAN) images, high-resolution multispectral resulting images are obtained. There is an urgent need to solve the problem of how to achieve higher spatial and spectral resolution in fused images. We propose an dual attention based dual regression network (DADR) architecture. The DADR network is mainly divided into three stages: feature extraction and feature fusion, image reconstruction, and dual regression network. Residual channel attention module (RCAM) and effective channel attention module (ECA) are added to the backbone network, so that the network can learn important spectral and spatial information and improve the ability of the network to retain information. The dual regression network solves the problem of adaptive and poor performance generated by the image reconstruction model. Because the dual regression network learns directly from feature fusion images, our model can better adapt to the data in real scenes and has stronger generalization ability. A series of experiments were conducted on GF-2, QB, and WV2 datasets, respectively. The validity of the DADR method was verified by quantitative comparison and qualitative analysis. Meanwhile, extensive experiments demonstrate that the DADR method is superior to other existing advanced pan-sharpening methods.