## 5 Discussion  
In the present work, we evaluate the three SOTA DL architectures designed for wildfire segmentation, three loss functions, and four image types to assess the impact of each of these factors in the segmentation performance of a model. We provide a comprehensive review and evaluate the U-Net-based _[PERSON]_ architecture [38], the FusionNet-based _[PERSON]_ architecture [5], and the VGG16-based _[PERSON]_ architecture [36], the Dice [26], Focal Tversky [41], Unified Focal [42] losses, and the visible and NIR images of the Corsican Fire Database [71] alongside two types of fused visible-NIR images produced by the methods by [PERSON] et al. [29] and [PERSON] et al. [34] for a total of thirty-six combinations. We evaluate these combinations through three metrics, namely the Matthews Correlation Coefficient [43], F1 score [83], and the Hafiane quality index [45]. Next, we obtain the top five best performing combinations across all metrics, with the _Akhloufi_ + _Dice_ + _visible_ scoring the best performance on all metrics by a close margin. However, after grouping the results by architecture, loss function, and image type, we observe that the _Akhloufi_ architecture and the Focal Tversky loss function have by far the most robust performance, displaying scant variance in their results. The performance of the combinations, when grouped by image type, displayed an almost identical behavior, pointing to a very little influence of the image type in the segmentation performance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Method** & **Value** \\ \hline \multirow{4}{*}{MCC} & [PERSON] + Focal Tversky + visible & 0.9225 \\  & [PERSON] + Focal Tversky + visible & 0.9241 \\  & [PERSON] + Focal Tversky + visible & 0.9240 \\  & **[PERSON] + Focal Tversky + visible** & **0.9244** \\ \hline \multirow{4}{*}{F1} & [PERSON] + Focal Tversky + visible & 0.9244 \\  & [PERSON] + Focal Tversky + visible & 0.9260 \\  & [PERSON] + Focal Tversky + visible & 0.9258 \\  & **MECA [PERSON] + Focal Tversky + visible** & **0.9263** \\ \hline \multirow{4}{*}{HAF} & [PERSON] + Focal Tversky + visible & 0.9004 \\  & [PERSON] + Focal Tversky + visible & 0.9016 \\ \cline{1-1}  & [PERSON] + Focal Tversky + visible & 0.9019 \\ \cline{1-1}  & **MECA Akhloufi + Focal Tversky + visible** & **0.9032** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison the best combination with and without attention modules. The best combination per metric is highlighted in bold. Figure 21: In-detail structure of the model proposed by [PERSON] et al. [38] with the inclusion of MECA modules. The numbers inside the squares represent the number of filters in the corresponding layer. We also obtain the Spearman correlation matrix for each parameter for all combinations to assess the monotonic relation between the evaluated architectures, loss functions, and image types concerning the scores of the three metrics. In this analysis, we find that the architecture and loss function parameters display high correlations with the three metrics, with the _Akhloufi_ architecture and Focal Tversky loss function showing a high positive correlation with these metrics. Additionally, we can observe that all image types show significantly weak correlations with the evaluation metrics, with the visible images showing a small positive correlation. In this correlation analysis, we can also see a near-perfect positive correlation between the MCC, F1, and HAF metrics. The latter means that, for future works, one of these metrics can suffice for the evaluation of the segmentation performance of a model. Taking into account the performance evaluation and correlation analysis, we consider the _Akhloufi_ + _Focal Tversky + visible_ combination as the best performing one and fine-tune its training hyperparameters, with a learning rate of \(10^{-4}\), ADAM optimizer, 50 training epochs, and a batch size of four showing the best results. In this regard, it is relevant to note that the batch size is, in our case, a hard constraint imposed by the employed hardware.