# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 4 Results
### Classification and Accuracy Assessment
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**RF Spring**} & \multicolumn{4}{c}{**Ground Truth**} & \multirow{2}{*}{**User’s accuracy**} \\ \cline{3-4} \multicolumn{1}{c}{} & **1** & **2** & **3** & **4** & **5** & **Total** & **User’s accuracy** \\ \hline \multirow{5}{*}{**Classified**} & **1** & 148 & 1 & 3 & 0 & 0 & 152 & 97.37\% \\  & **2** & 1 & 104 & 1 & 0 & 0 & 106 & 98.11\% \\  & **3** & 0 & 0 & 43 & 2 & 0 & 45 & 95.56\% \\  & **4** & 0 & 0 & 0 & 8 & 0 & 8 & 100\% \\  & **5** & 1 & 0 & 1 & 0 & 96 & 98 & 97.96\% \\
**Total** & 150 & 105 & 48 & 10 & 96 & 479 & \\
**Producer’s accuracy** & 98\% & 91.43\% & 79.17\% & 80\% & 100\% & \\ \hline \hline \multicolumn{1}{c}{**RF Summer**} & \multicolumn{4}{c}{**Ground Truth**} & \multirow{2}{*}{**User’s accuracy**} \\ \cline{3-4} \multicolumn{1}{c}{} & **1** & **2** & **3** & **4** & **5** & **Total** & **User’s accuracy** \\ \hline \multirow{5}{*}{**Classified**} & **1** & 155 & 0 & 2 & 0 & 0 & 157 & 98.73\% \\  & **2** & 5 & 40 & 2 & 0 & 3 & 50 & 80.00\% \\  & **3** & 0 & 0 & 97 & 0 & 1 & 98 & 98.98\% \\  & **4** & 0 & 0 & 2 & 5 & 0 & 7 & 71.43\% \\
**5** & 2 & 0 & 3 & 0 & 75 & 80 & 93.75\% \\
**Total** & 162 & 40 & 106 & 5 & 79 & 392 & \\
**Producer’s accuracy** & 95.68\% & 100\% & 91.51\% & 100\% & 94.94\% & \\ \hline \hline \multicolumn{1}{c}{**SVM Summer**} & \multicolumn{4}{c}{**Ground Truth**} & \multirow{2}{*}{**User’s accuracy**} \\ \cline{3-4} \multicolumn{1}{c}{} & **1** & **2** & **3** & **4** & **5** & **Total** & **User’s accuracy** \\ \hline \multirow{5}{*}{**Classified**} & **1** & 151 & 1 & 3 & 0 & 2 & 157 & 96.18\% \\  & **2** & 1 & 33 & 10 & 0 & 6 & 50 & 66\% \\
**3** & 0 & 1 & 94 & 0 & 3 & 98 & 95.92\% \\  & **4** & 0 & 0 & 2 & 5 & 0 & 7 & 71.43\% \\
**5** & 3 & 10 & 1 & 0 & 66 & 80 & 82.50\% \\
**Total** & 155 & 45 & 110 & 5 & 77 & 392 & \\
**Producer’s accuracy** & 97.42\% & 73.33\% & 85.45\% & 100\% & 85.71\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrices for both classification algorithms, random forest (RF) and support vector machines (SVM), and for both flights (spring and summer). Land cover classes are denoted as follows: 1 = cork oak; 2 = shrubs; 3 = grass; 4 = soil; 5 = shadows. is classified with a user's accuracy of 95.56% (spring) and 98.98% (summer) for RF and 79.17% (spring) and 95.92% (summer) for SVM. The producer's accuracy reaches 89.58% (spring) and 91.51% (summer) with RF, and 79.17% (spring) and 85.45% (summer) for SVM. Table 4 indicates an overall accuracy of 97.6% for RF and 94.1% for SVM in the spring image, with kappa values ranging from 0.973 to 0.935 for RF and SVM, respectively. The overall accuracy for the summer mosaic is 94.9% for RF and 89% for SVM, with kappa values of around 0.928 for RF and 0.847 for SVM. Both classifiers (RF and SVM) did not require particular computational resources. Both imagery classification steps, including the training phase, took a few seconds (from 1 to 10 seconds; in this computing time, we excluded the selection of trainers that we performed manually).