**5. Discussion**

#### *5.1. E*ff*ect of the Openness*

In this section, we compare the robustness of the proposed Pareto approach over several openness values. The performance of the method was measured with different numbers of classes between the source and domain. As the value of openness increased, the number of unknown samples also increased which was more difficult for the classifier, compared to classifying only shared classes in the closed set classification. Table 8 shows the results obtained using different openness values for the VHR dataset. In the first scenario, we removed three classes from each source dataset corresponding to an openness of 25%. This means that the source dataset contained nine classes while the target dataset contained 12 classes (three were unknown). The highest OS accuracy was 88.33% achieved by the Merced→ AID scenario, while the lowest accuracy 67.75% achieved by the Merced→ NWPU scenario, which was 14.59% higher than the non-adaptation approach for the same scenario. The second scenario we removed four classes from the source dataset, which led to eight classes in the source dataset and 12 classes in the target dataset (four were unknown). In this scenario, the value of the accuracy degraded, resulting in 80.58% as the highest from the Merced→ AID scenario and 69.91% as the lowest accuracy from the AID→ NWPU scenario. The third and fourth scenario, we removed five and six classes, respectively. The results showed that although the accuracy decreased in both scenarios, the results were still better than the non-adaptation approach. When computing the average accuracy for all six scenarios, the Pareto method achieved higher accuracy than the approach with no adaptation in all values of openness, even with an openness of 50% the Pareto method achieved an average of 63.80% accuracy with a 24.65% increase than the non-adaptation method for the same openness.


**Table 8.** Sensitivity analysis with respect to the openness for the VHR dataset. Results are expressed in terms of open set accuracy (OS) (%) and average accuracy (AA) (%).

The results of the EHR dataset, shown in Table 9 were achieved using different openness values. In the first scenario, we removed three classes from the source domain while the target domain contained seven classes leading to an openness of 42.85%. The scenario Trento→Vaihingen resulted in 82.02% accuracy higher than the Vaihingen→Trento which resulted in 78.52% for the openness of 42.85%. For this scenario, the Pareto approach was 40.52% higher in accuracy than the non-adaptation method which resulted in an average 39.75% accuracy for the 42.85% openness. For the second scenario we removed four classes from the source dataset. The accuracy degraded in this scenario to the values of 60.11% and 48.33% for the two scenarios, respectively. The third scenario, we removed five classes from the source dataset, resulting in an openness of 71.14%. The results of the accuracy decreased to 51.66% achieved by the Trento→Vaihingen scenario. As a conclusion, we found that the proposed approach outperforms the accuracy of the non-adaptation method for different values of openness with at least 20.18%.

**Table 9.** Sensitivity analysis with respect to the openness for the EHR dataset. Results are expressed in terms of OS (%) and AA (%).


#### *5.2. E*ff*ect of the Reconstruction Loss*

Table 10 shows the results of the proposed method with setting the regularization parameter λ to different values in the range [0,1] for the VHR dataset. We made three scenarios with regularization parameter values of 0, 0.5, and 1. For the first scenario, the λ was set to 0, which corresponds to the removal of the decoder part. The average accuracy dropped to 78.94%, which indicated the importance of the decoder part in the proposed method. As we can see from Table 10, setting the regularization parameter to 1 resulted in the best accuracy percentage for all scenarios except the NWPU→ AID which gave the highest accuracy 89.5%, when the regularization parameter was set to 0.5. The average accuracy (AA) results in Table 10 suggested that setting the regularization parameter to 1 gives better accuracy results.


**Table 10.** Sensitivity analysis with respect to the regularization parameter for the VHR dataset. Results are expressed in terms of OS (%) and AA (%).

The results in Table 11 show the effect of setting the regularization parameter λ to different values in the range [0,1] for the EHR dataset. We made three scenarios with regularization parameter values of 0, 0.5, and 1. The first scenario, we removed the decoder part by setting the regularization parameter λ to 0. The second and third scenario, the regularization parameter was set to 0.5 and 1, respectively. For the Trento→Vaihingen scenario, removing the decoder resulted in an accuracy of 66.54%, which was a noticeable decrease from the highest accuracy 82.02% achieved when the regularization parameter was set to 1. The second scenario Vaihingen→Trento also resulted in the better accuracy with the regularization parameter 1, while setting the regularization to 0.5 resulted in the worst accuracy of 51.30%. From the results shown in Table 11, setting the regularization parameter to 1 gave better accuracy results.

**Table 11.** Sensitivity analysis with respect to the regularization parameter for the EHR dataset. Results are expressed in terms of OS (%) and AA (%).

