**6. Conclusions**

In this paper, we addressed the problem of open-set domain adaptation in remote sensing imagery. Different to the widely known closed set domain adaptation, open set domain adaptation shares a subset of classes between the source and target domains, whereas some of the target domain samples are unknown to the source domain. Our proposed method aims to leverage the domain discrepancy between source and target domains using adversarial learning, while detecting the samples of the unknown class using a pareto-based raking scheme, which relies on the two metrics based on distance and entropy. Experiment results obtained on several remote sensing datasets showed promising performance of our model, the proposed method resulted an 82.64% openset accuracy for the VHR dataset, outperforming the method with no-adaptation by 23.06%. In the EHR dataset, the pareto approach resulted an 80.27% accuracy for the openset accuracy. For future developments, we plan to investigate other criteria for identifying the unknown samples to improve further the performance of the model. In addition, we plan to extend this method to more general domain adaptation problems such as the universal domain adaptation.

**Author Contributions:** R.A., Y.B. designed and implemented the method, and wrote the paper. H.A., N.A. contributed to the analysis of the experimental results and paper writing. All authors have read and agreed to the published version of the manuscript.

**Funding:** Deanship of Scientific Research at King Saud University. **Acknowledgments:** The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group no (RG-1441-055).

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