**5. Conclusions**

In this paper, we presented a novel model average network for pansharpening, and is referred to as PWNet. The proposed PWNet attempts to integrate the complementary characteristics of the CS-based and MRA-based methods through an end-to-end trainable neural networks, and thus it is data-driven and able to adaptively weight the results of the classical methods depending on their performances. Experiments on several data sets collected by three kinds of satellites demonstrate that the pansharpened HRMS images by the proposed PWNet can not only enhance the spatial qualities but also can keep the spectral information of the original MS images. In addition, the proposed PWNet has some distribution structures. Thus, we will extend the proposed model to a distribution version by using the techniques of distributed processing [53] to further reduce the running time while maintaining the quality of the results.

**Author Contributions:** J.L. and C.Z. (Chunxia Zhang) structured and drafted the manuscript with assistance from C.Z. (Changsheng Zhou); Y.F. conceived the experiments and generated the graphics with assistance from C.Z. (Changsheng Zhou). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Key Research and Development Program of China under Grant 2018AAA0102201, and by the National Natural Science Foundation of China gran<sup>t</sup> number 61877049.

**Acknowledgments:** The authors would like to thank Gemine Vivone for sharing the pansharpening Matlab toolbox [3], to Giuseppe Scarpa for providing their codes of PNN in [32], to Wei and Yuan in [42,43] for sharing their CNN-based codes for pansharpening.

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