# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 4 Results and Discussion
### Performance Statistics
#### 4.2.2 DELTA Dataset  
Table 5 presents the best model performance for the validation set and Table 6 the best model performance for the test set. It is observed that the classifiers have better performance for the DELTA dataset than for the POST dataset, seemingly due to the fact that DELTA  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **DICE** & **Accuracy** & **Omission** & **Commission** \\ \hline RF & 0.93 & 0.92 & 0.08 & 0.08 \\ SVM & 0.86 & 0.88 & 0.12 & 0.10 \\ LR & 0.88 & 0.88 & 0.12 & 0.10 \\ MLP & 0.80 & 0.85 & 0.01 & 0.12 \\ ELM & 0.89 & 0.89 & 0.01 & 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Best model performance for the POST dataset applied over the Test set.  
Figure 4: Separability of classes in bands and indices for POST and DELTA datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **DICE** & **Accuracy** & **Omission** & **Commission** \\ \hline RF & 0.92 & 0.93 & 0.08 & 0.09 \\ SVM & 0.87 & 0.88 & 0.16 & 0.09 \\ LR & 0.88 & 0.89 & 0.14 & 0.11 \\ MLP & 0.87 & 0.87 & 0.10 & 0.14 \\ ELM & 0.92 & 0.92 & 0.02 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Best model performance for the POST dataset applied over the validation set.  
shows higher levels of SI in almost all the features analyzed (Figure 4). Moreover, the order of performance of the algorithms is maintained for the POST dataset, showing a similar decreasing tendency for the test dataset indicators.  
In [7], similar results were obtained when RF was compared to SVM and MLP over MODIS data with 500 m resolution. In this study, the worst performance for most algorithms occurred for the POST dataset. This may have been caused by the high variability of the burned class [58]. On the contrary, this variability was considerably reduced for the DELTA dataset, which accounts for the better performance of the algorithms [52]. The problems mentioned in the POST dataset could be reduced, including a larger amount of training samples of different land covers and burned zones with different conditions [59]. Although the data used for the training sets were thoroughly revised, a high number of pixels with atypical values were found, which could have been wrongly classified. This highlights the importance of adopting a strategy of several steps where the results obtained are refined through the elimination or inclusion of training areas in the most conflicting zones [59].  
Mostly, omission errors had a greater magnitude over the DELTA sets than over the POST sets. The opposite took place for commission errors. Omissions can be explained by the high variability of burned areas, which present zones of different levels of severity and cover, such as shrublands and agricultural burning. Some of them present both low contrast with their unburned environment and areas in which vegetation has begun recovering after the fire [9]. On the other hand, commission errors were caused by the topographic shading that was not completely removed by the correction conducted, the presence of elements of great height (trees that cast shadows on border areas) [59], areas with scarce vegetation and high presence of soil (agricultural and forest harvested), whose spectral changes are consistent with burned areas [60].