**6. Conclusions**

In this paper, we proposed a dual-branch network named SSIN with spectral-spatial interaction for pansharpening. In the proposed SSIN, the PAN and the LRMS images are processed separately to fully extract their features. SSIN extracted spatial information from the PAN image and spectral information from the MS image. To make the most of the spatialspectral information, we propose an information interaction block based on a dual-branch network to promote the interaction between spectral and spatial information. Furthermore, the spectral-spatial attention module is used to guide information integration and enhance the characteristics of the another branch. The performance improvement of the two modules for the dual-branch network was proved in the ablation study. Moreover, we used pixel attention in the information fusion module to adjust the importance of each pixel in the feature maps, thereby further improving the network performance. Extensive experiments have demonstrated the effectiveness of our proposed method on pansharpening.

**Author Contributions:** Conceptualization, Z.N.; methodology, Z.N.; software, Z.N. and L.C.; validation, Z.N. and L.C.; formal analysis, L.C.; investigation, S.J., X.Y.; resources, S.J., X.Y.; data curation, X.Y.; writing—original draft preparation, Z.N.; writing—review and editing, Z.N.; visualization, Z.N. and L.C.; supervision, S.J., X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research in our paper is sponsored by the funding from Science Foundation of Sichuan Science and Technology Department (2021YFH0119) and Sichuan University under gran<sup>t</sup> 2020SCUNG205.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We thank all the editors and reviewers in advance for their valuable comments that will improve the presentation of this paper.

**Conflicts of Interest:** The authors declare no conflict of interest.
