# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 4 Results
### Classification and Accuracy Assessment  
In the training phase, 586 polygons for the spring image and 1067 polygons for the summer image (corresponding to 2.18% and 2.15% of the total number, respectively) were selected. Details on the surface distribution and spectral characteristics of the training and validation polygons according to the five land-use classes are provided in Appendix A (Tables A3 and A4). The two resulting land cover maps are shown in Figures 5 and 6. Figure 5: Maps of the land cover classification for the spring flight obtained from random forest (RF) (**A1**) and support vector machine (SVM) (**A2**) algorithms. Bar charts show the surface distribution according to the five defined land cover classes. The incidence of the cork oak class is almost the same for both classifiers and in both flights, with values ranging between just over 40% for the RF classifier (40.10% spring, 40.31% summer) and nearly 39% for SVM (38.95% spring, 38.58% summer). The shrubs class represents about 10% according to RF in both spring and summer imagery, while for the SVM algorithm, shrubs form about 12% of the classified area. The grass class accounts for about 32.5% of the area (32.83% RF, 32.24% SVM) from the spring flight, while for the summer flight, this value is about 29% (28.95% RF, 29.22% SVM). Shadows cover 15.03% (RF) and 14.91% (SVM) in the spring image, while in the summer image, shadows amount to just over 19% (19.23% RF, 19.47% SVM). The class representing bare soil does not exceed 1.62%. Table 3 reports the confusion matrices for the accuracy assessment, while Table 4 reports the kappa values and the overall accuracy for both RF and SVM classification algorithms and for both spring and summer flights. On the basis of 479 and 393 validation polygons for spring and summer flights, respectively, the user's accuracy for the cork oak class is 97.37% (spring) and 98.73% (summer) for RF, while for SVM it is 98% (spring) and 96.18% (summer). The values of the producer's accuracy are 98.67% (spring) and 95.68% (summer) for RF, and 98% (spring) and 97.42% (summer) for SVM. Figure 6: Maps of the land cover classification for the summer flight obtained from random forest (RF) (**B1**) and support vector machine (SVM) (**B2**) algorithms. Bar charts show the surface distribution according to the five defined land cover classes. For the shrubs class, the user's accuracy of the RF classifier is between 80% (summer) and about 98% (spring), and for SVM it is about 90% in the spring flight, while it dropped to 66% in the summer flight. A similar difference is registered for the producer's accuracy, whose values are close to 100% for RF, while for SVM, they are 91.43% and 73.33% in spring and summer, respectively. The grass class \begin{table}
\begin{tabular}{c c c} \hline \hline
**Imagery** & **Kappa Value** & **Overall Accuracy** \\ \hline RF Spring & 0.973 & 97.6\% \\ SVM Spring & 0.935 & 94.1\% \\ RF Summer & 0.928 & 94.9\% \\ SVM Summer & 0.847 & 89.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Kappa index and overall accuracy for both classification algorithms, random forest (RF) and support vector machine (SVM), and for both flights (spring and summer).