## 4 Results
### Pa-PCA-Ca Structure  
As mentioned in Section 3.3, the current study employed the PCA technique on the output PMs of different classes. This was done due to the high PCC observed between the output PMs of different branches within each class (Figure 7). For instance, in the 'Wheat' class, the PM map of the FC\({}_{2}\) branch demonstrated a PCC of 0.89 with the PM of the FC\({}_{1}\) branch. The PCC values were 0.85, 0.91, and 0.65 when comparing the PM of FC\({}_{2}\) with FC\({}_{3}\), FC\({}_{4}\), and FC\({}_{5}\), respectively. Notably, the optical FCs exhibited stronger correlation among themselves compared to the SAR-based FC (FC\({}_{5}\)). The PCCs between the output PMs of optical FCs (FC\({}_{1-4}\)) ranged from 0.71 (FC\({}_{3}\) against FC\({}_{2}\) and FC\({}_{4}\) in the corn class) to 0.96 (FC\({}_{3}\) against FC\({}_{4}\) in the garden class). However, the PCCs between SAR-based FCs (FC\({}_{5}\)) and optical FCs (FC\({}_{1-4}\)) ranged from 0.58 (FC\({}_{5}\) against FC\({}_{1}\) and FC\({}_{2}\) in the 'Corn' and 'Alfalfa' classes, respectively) to 0.85 (FC\({}_{5}\) against FC\({}_{3}\) in the 'Garden' class). Based on these PCCs, it can be concluded that the PMs of different branches exhibited a high correlation, leading to redundancy in the input FCs of the RF model in the Ca structure. Therefore, employing PCA on the outputs of the Pa structure was necessary to enhance the performance of the Meta-model. To employ PCA on the output PMs of the Pa structure and feed them into the Ca structure, the top 'n' components were selected for each class. To determine the optimal  
Figure 7: PCC between different output PMs of five Pa branches (FC\({}_{1-5}\)) for seven target classes (wheat, corn, beet, onion, alfalfa, garden, and other). value for 'n', as described in Section 3.3, a grid search was conducted using 5-fold cross-validation on the training data. Table 6 presents the effect of 'n' on OA and Kappa. As shown, when 'n' was set to 1 (using only the first output component of PCA for each class), the classification accuracy experienced a 4.88% increase in OA (from 92.56% to 97.44%) and 0.050 increase in Kappa (from 0.911 to 0.961) compared to the Pa-Ca structure. This indicated that PCA led to a significant improvement in Meta-model performance. Increasing the value of 'n' to values of more than 1 did not guarantee higher accuracies compared to the Pa-Ca case. Therefore, 'n' was set to 1 in the proposed methodology, and the remaining results in this article are based on this value. Figure 8 compares the UA and PA of different target classes for the proposed methodology (Pa-PCA-Ca (\(n=1\))) with the Pa-Ca structure. As can be seen, considering the UA metric, in the Pa-Ca model, the 'Beet' class achieved the highest UA of 94.15%, while the 'Onion' class achieved the lowest UA of 86.31%. However, employing the PCA technique led to a significant increase in accuracy for all classes, where the 'Wheat' class achieved the highest UA of 97.54%, while the 'Other' class achieved the lowest UA of 91.97%. The highest increase was observed in the 'Onion' class, with an improvement of 9.29% from 86.31% to 95.60%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**n** & **1** & **1-2** & **1-3** & **1-4** & **1-5** & **Pa-Ca** \\ \hline OA (\%) & 97.44 & 95.32 & 93.88 & 92.18 & 90.73 & 92.56 \\ Kappa & 0.961 & 0.937 & 0.915 & 0.909 & 0.887 & 0.911 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Effect of the ‘n’ (number of components) on the classification accuracy (OA and Kappa) using 5-fold cross-validation on training data. Figure 8: Comparison of the UA and PA of different target classes in the proposed method (Pa-PCA-Ca (\(n=1\))) and Pa-Ca ensemble structure using the validation dataset. The same conclusions can also be drawn for the PA metric (Figure 8). Employing PCA resulted in a significant improvement in accuracy for all classes, with the 'Onion' class showing the highest improvement of 6.55% from 90.11% to 96.66%.