## 3 Results  
The OA values obtained from the various classification approaches are compared in Table 5. The same table reports the selected features (total number, bands, and spectral and textural indices) considering their importance in each RF classification. Results of the statistical analysis of accuracy results, based on the McNemar tests, are reported in Supplementary Materials, Table S1. The only S2-derived dataset produces good accuracy from a typical RF PB method (PB_S2: 74.4%). Applying the OB approach to the same dataset, using S2-derived textures (OB_S2) substantially improves the classification obtained with the PB approach (81.6 vs. 74.4%, \(p=0.005\)). The addition of S1 bands to the S2 dataset in the PB approach (PB_S2S1) produces a more relevant accuracy increase than PB_S2 (81.2 vs. 74.4%, \(p=0.003\)). The OB approach on the same dataset (OB_S2S1) gives an apparent increase compared to the corresponding PB method (PB_S2S1) but is not statistically significant (83.5 vs. 81.2%, \(p=0.401\)). Significative improvements, compared to PB_S2S1, are obtained when S1 is used for additional textural indices in the OB classification (OB_S2S1T) (86.2 vs. 81.2%, \(p=0.033\)). Despite producing a 4.77-m resolution output, the PB method applied on only multispectral PL data (PB_PL) yields worse thematic accuracy results than the same approach applied to the S2 dataset (PB_S2) (66.7 vs. 74.4%, \(p=0.014\)) or the S2 and S1 combined dataset (PB_S2S1) (66.7% vs. 81.2%, \(p=0.000\)). When the OB approach is applied to the PL single-sensor dataset (OB_PL), the OA increases compared to the PB_PL (82.2 vs. 66.7%, \(p=0.000\)). When OB_PL is compared with the same approach applied to the S2 data (OB_S2), no significant differences arise in the thematic accuracy (82.2 vs. 81.6 %, \(p=0.892\)). Adopting the three-sensor dataset with a PB classification (PB_S2S1 PL) produces a 4.77-m spatial resolution output but does not improve the OA compared to using only S2 and S1 data (PB_S2S1) (81.6 vs. 81.2%, \(p=1.000\)). Using S2 and S1 data for the spectral information and PL data for the textural information and the object delineation in the OB method (OB_S2S1 PL) noticeably increases the OA compared to the PB approach applied to the same three-sensor dataset (PB_S2S1 PL) (88.3 vs. 81.6%, \(p=0.002\)). Using the spectral bands of PL instead of those of S2 in a similar three-sensor dataset (OB_PLS2S1) apparently improves the OA compared to the previous approach (OB_S2S1 PL), but the difference is not statistically significant (90.6 vs. 88.3%, \(p=0.23\)). In contrast, the latter approaches (OB_S2S1 PL and OB_PLS1S2) yield statistically significant improvements compared to the same method applied to the S2-S1 dataset (OB_S2S1) (88.3 vs. 83.5%, \(p=0.004\); 90.6 vs. 83.5%, \(p=0.002\)). Unlike OB_S2S1 PL, OB_PLS1S2 produces significative OA improvements compared to the OB_S2S1T, which, as indicated, includes some textural indices derived from S1 data (90.6 vs. 86.3%, \(p=0.05\)). A cartographical comparison between the various classification approaches' outputs is shown for a representative portion of the study area characterized by a built-up area, a lake portion, and a neighboring area composed of grasslands, shrublands, and scattered woodlands (Figure 3). Despite the final application of a majority filter, the output of the PB approaches suffer from the typical salt-and-pepper effect (Figure 3c-f). An evident commission between built-up areas and croplands can be detected in all the PB approaches, including that based on the three-sensor dataset. The addition of the S1 bands partially mitigates this issue (Figure 3d,f). The application of the OB approaches improves the classification results in all the studied approaches. The last two output maps, obtained from the OB procedure and the data integration of all three datasets, confirm the superior performance of these classification approaches.